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Stacha, Radek. "Optimalizace kogeneračního systému." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2014. http://www.nusl.cz/ntk/nusl-231502.
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Master thesis is focused on optimization of cogeneration system for purpose of rating optimization methods and evaluating properties of these methods. For each method there is description together with block schemes. First part of thesis is devoted to description of methods and their comparison. Second part consists of development of hybrid algorithm, which is used to optimize cogeneration systém model. Each algorithm compared is together with hybrid algorithms included in annexes.
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Hrabec, Dušan. "Mathematical Programs for Dynamic Pricing - Demand Based Management." Doctoral thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2017. http://www.nusl.cz/ntk/nusl-263401.
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Tato disertační práce se zabývá vývojem, modelováním a analýzou poptávkově orientovaných úloh, které zahrnují marketingová, operační a logistická rozhodnutí. Úlohy jsou zvoleny tak, aby mohly být dále rozšířeny o koncept tzv. dynamického oceňování a jiných dynamických marketingových rozhodnutí. V práci jsou využity dvě základní poptávkově orientované úlohy: a) úloha kolportéra novin, která je zvolena pro její jednoduchou formu a která tak slouží jako nástroj pro ilustrativní ukázky rozhodovacích procesů v podobných typech úloh, a b) úloha návrhu dopravní sítě, kde jsou využity některé výsledky a znalosti získané při řešení úlohy kolportéra novin. Kolportér (či obecně maloobchodník) čelí náhodné poptávce, která může být postupně ovlivněna oceňováním, marketingovými (tj. reklamními) rozhodnutími a nakonec jejich kombinací. Poptávka obsahuje tedy náhodnou složku, která je pomocí přístupů stochastické optimalizace modelována ve specifickém tvaru (tj. aditivní či multiplikativní tvar). Závislost cena-poptávka je zachycena pomocí nelineární klesající poptávkové funkce, zatímco (vhodná) reklama vede ke zvýšení poptávky (běžně rostoucí s-křivka či konkávní funkce). Výsledky získané při řešení úlohy kolportéra novin s oceňováním jsou následně využity v úloze návrhu dopravní sítě. Tato stochastická úloha je modelována (reformulována) pomocí dvou přístupů stochastické optimalizace: wait-and-see přístup a here-and-now přístup. Jelikož tato implementace vede na lineární či nelineární celočíselnou (navíc scénářovou) úlohu, jsou v práci zmíněny taky výpočetní nástroje. Autor pro řešení používá (původní) tzv. hybridní algoritmus, což je kombinace heuristického (genetického) algoritmu a nástroje optimalizačního softwaru. Potenciální aplikace sestavených modelů, obzvláště v oblasti odpadového hospodářství, jsou diskutovány v závěrečné části disertační práce.
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Šandera, Čeněk. "Hybridní model metaheuristických algoritmů." Doctoral thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2015. http://www.nusl.cz/ntk/nusl-234259.
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The main topic of this PhD thesis is metaheuristic algorithm in wider scope. The first chapters are dedicated to a description of broader context of metaheuristics, i.e. various optimization classes, determination of their omplexity and different approaches to their solutions. The consequent discussion about metaheuristics and their typical characteristics is followed by several selected examples of metaheuristics concepts. The observed characteristics serve as a base for building general metaheuristics model which is suitable for developing brand new or hybrid algorithms. The thesis is concluded by illustration of author’s publications with discussion about their adaptation to the proposed model. On the attached CD, there is also available a program implementation of the created model.
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Hachimi, Hanaa. "Hybridations d'algorithmes métaheuristiques en optimisation globale et leurs applications." Phd thesis, INSA de Rouen, 2013. http://tel.archives-ouvertes.fr/tel-00905604.
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L'optimisation des structures est un processus essentiel dans la conception des systèmes mécaniques et électroniques. Cette thèse s'intéresse à la résolution des problèmes mono-objectifs et multi-objectifs des structures mécaniques et mécatroniques. En effet, les industriels ne sont pas seulement préoccupés à améliorer les performances mécaniques des pièces qu'ils conçoivent, mais ils cherchent aussi à optimiser leurs poids, leurs tailles, ainsi que leurs coûts de production. Pour résoudre ce type de problème, nous avons fait appel à des métaheuristiques robustes qui nous permettent de minimiser le coût de production de la structure mécanique et de maximiser le cycle de vie de la structure. Alors que des méthodes inappropriées de l'évolution sont plus difficiles à appliquer à des modèles mécaniques complexes en raison de temps calcul exponentiel. Il est connu que les algorithmes génétiques sont très efficaces pour les problèmes NP-difficiles, mais ils sont très lourds et trop gourmands quant au temps de calcul, d'où l'idée d'hybridation de notre algorithme génétique par l'algorithme d'optimisation par essaim de particules (PSO) qui est plus rapide par rapport à l'algorithme génétique (GA). Dans notre expérimentation, nous avons obtenu une amélioration de la fonction objectif et aussi une grande amélioration de la minimisation de temps de calcul. Cependant, notre hybridation est une idée originale, car elle est différente des travaux existants. Concernant l'avantage de l'hybridation, il s'agit généralement de trois méthodes : l'hybridation en série, l'hybridation en parallèle et l'hybridation par insertion. Nous avons opté pour l'hybridation par insertion par ce qu'elle est nouvelle et efficace. En effet, les algorithmes génétiques se composent de trois étapes principales : la sélection, le croisement et la mutation. Dans notre cas, nous remplaçons les opérateurs de mutation par l'optimisation par essaim de particules. Le but de cette hybridation est de réduire le temps de calcul ainsi que l'amélioration la solution optimale.
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Horáková, Pavla. "Optimalizace pro registraci obrazů založená na genetických algoritmech." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012. http://www.nusl.cz/ntk/nusl-219505.
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Diploma thesis is focused on global optimization methods and their utilization for medical image registration. The main aim is creation of the genetic algorithm and test its functionality on synthetic data. Besides test functions and test figures algorithm was subjected to real medical images. For this purpose was created graphical user interface with choise of parameters according to actual requirement. After adding an iterative gradient method it became of hybrid genetic algorithm.
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Naldi, Murilo Coelho. "Agrupamento híbrido de dados utilizando algoritmos genéticos." Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-07112006-080351/.
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Técnicas de Agrupamento vêm obtendo bons resultados quando utilizados em diversos problemas de análise de dados, como, por exemplo, a análise de dados de expressão gênica. Porém, uma mesma técnica de agrupamento utilizada em um mesmo conjunto de dados pode resultar em diferentes formas de agrupar esses dados, devido aos possíveis agrupamentos iniciais ou à utilização de diferentes valores para seus parâmetros livres. Assim, a obtenção de um bom agrupamento pode ser visto como um processo de otimização. Esse processo procura escolher bons agrupamentos iniciais e encontrar o melhor conjunto de valores para os parâmetros livres. Por serem métodos de busca global, Algoritmos Genéticos podem ser utilizados durante esse processo de otimização. O objetivo desse projeto de pesquisa é investigar a utilização de Técnicas de Agrupamento em conjunto com Algoritmos Genéticos para aprimorar a qualidade dos grupos encontrados por algoritmos de agrupamento, principalmente o k-médias. Esta investigação será realizada utilizando como aplicação a análise de dados de expressão gênica. Essa dissertação de mestrado apresenta uma revisão bibliográfica sobre os temas abordados no projeto, a descrição da metodologia utilizada, seu desenvolvimento e uma análise dos resultados obtidos.Clustering techniques have been obtaining good results when used in several data analysis problems, like, for example, gene expression data analysis. However, the same clustering technique used for the same data set can result in different ways of clustering the data, due to the possible initial clustering or the use of different values for the free parameters. Thus, the obtainment of a good clustering can be seen as an optimization process. This process tries to obtain good clustering by selecting the best values for the free parameters. For being global search methods, Genetic Algorithms have been successfully used during the optimization process. The goal of this research project is to investigate the use of clustering techniques together with Genetic Algorithms to improve the quality of the clusters found by clustering algorithms, mainly the k-means. This investigation was carried out using as application the analysis of gene expression data, a Bioinformatics problem. This dissertation presents a bibliographic review of the issues covered in the project, the description of the methodology followed, its development and an analysis of the results obtained.
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Gomes, Wellison José de Santana. "Estudo do efeito de incertezas na otimização estrutural." Universidade de São Paulo, 2010. http://www.teses.usp.br/teses/disponiveis/18/18134/tde-23032010-092000/.
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Este trabalho apresenta um estudo do efeito de incertezas na otimização estrutural. Tal efeito pode ser quantificado em termos de probabilidades de falha bem como do risco, ou custo esperado de falha. O estudo se baseia na comparação dos resultados obtidos através de três distintas formulações do problema de otimização estrutural: otimização determinística, otimização baseada em confiabilidade e otimização de risco estrutural. Para efeitos de comparação, informações sobre risco de falha estrutural (produto da probabilidade de falha pelo custo de falha) são incorporadas nas três formulações. A otimização determinística (DDO - Deterministic Design Optimization) permite encontrar uma configuração estrutural que é ótima em termos mecânicos, mas não considera explicitamente a incerteza dos parâmetros e seus efeitos na segurança estrutural. Em conseqüência, a segurança da estrutura ótima pode ser comprometida, em comparação à segurança da estrutura original. A otimização baseada em confiabilidade (RBDO - Reliability-Based Design Optimization) garante que a estrutura ótima mantenha um nível mínimo (e mensurável) de segurança. Entretanto, os resultados são dependentes da probabilidade de falha usada como restrição na análise. A otimização de risco estrutural (RBRO - Reliability-Based Risk Optimization) aumenta o escopo do problema, buscando um balanço entre economia e segurança, objetivos estes que de uma forma geral competem entre si. Isto é possível através da quantificação de custos associados à construção, operação e manutenção da estrutura, bem como das consequências monetárias de falha. A experiência mostra que problemas de otimização estudados, são utilizados neste trabalho dois métodos de otimização heurísticos: algoritmos genéticos e método do enxame de partículas. Tendo a eficiência como objetivo, dois métodos com fundamentação matemática também são estudados: os métodos de Powell e de Polak-Ribiere. Finalmente, buscando uma relação de compromisso entre confiabilidade (capacidade de encontrar o mínimo global em todos os problemas) e eficiência, quatro algoritmos híbridos são construídos, combinando os quatro métodos citados anteriormente. Efeitos de incertezas na otimização estrutural são estudados através da comparação de soluções obtidas via diferentes formulações do problema de otimização. São apresentados alguns estudos de caso, enfatizando as diferenças entre os projetos ótimos obtidos por cada formulação. O estudo mostra que, em geral, a estrutura ótima só é encontrada pela formulação mais abrangente: a otimização de risco ou RBRO. O estudo mostra que, para que a formulação DDO encontre a mesma configuração ótima da formulação RBRO, é necessário especificar um coeficiente de segurança ótimo para cada modo de falha. De maneira semelhante, o estudo mostra que quando os custos associados a diferentes modos de falha são distintos, a formulação RBDO somente resulta na estrutura ótima quando uma probabilidade de falha ótima é especificada como restrição para cada modo falha da estrutura.In this study the effects of uncertainty on optimum structural design are investigated, by comparing three distinct formulations of a structural optimization problem. Such effects can be quantified in terms of failure probabilities and risk, or expected costs of failure. Deterministic Design Optimization (DDO) allows one the find the shape or configuration of a structure that is optimum in terms of mechanics, but the formulation do not consider explicitly parameter uncertainty and its effects on structural safety. As a consequence, safety of the optimum structure can be compromised, in comparison to safety of the original structure. Reliability-based Design Optimization (RBDO) has emerged as an alternative to properly model the safety-under-uncertainty part of the problem. With RBDO, one can ensure that a minimum (and measurable) level of safety is achieved by the optimum structure. However, results are dependent on the failure probability used as constraint in the analysis. Risk optimization increases the scope of the problem, by addressing the compromising goals of economy and safety, and allowing one to find a proper point of balance between these goals. This is accomplished by quantifying the costs associated to construction, operation and maintenance of the structure, as well as the monetary consequences of failure. Experience shows that structural optimization problems can have multiple local minima. With the objective of finding the global minimum in all studied problems, two heuristic optimization methods are used in this study: genetic algorithms and particle swarm optimization. Aiming at efficiency, two methods with mathematical foundations are also considered: the methods of Powel and Polak-Ribiere. Finally, looking for a compromise between reliability (capacity to find the global minimum) and efficiency, four hybrid algorithms are constructed, combining the four methods just cited. The study investigates the effects of uncertainty on optimum structural design by comparing solutions obtained via the different formulations of the optimization problem. The paper presents some case studies, highlighting the differences in the optimum designs obtained with each formulation. The study leads to a better understanding of the limitations of each formulation in the solution of structural optimization problems. The investigation shows that, in general, the optimum structure can only be found by the most comprehensive formulation: risk optimization or RBRO. The study shows that DDO only leads to the optimum structure if an optimum safety coefficient is used as constraint for each individual failure mode. In a similar way, the investigation shows that when the costs associated to distinct failure modes are different, the RBDO formulation only leads to the optimum structural design if an optimum failure probability is specified as constraint for each failure mode of the structure.
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Rebreyend, Pascal. "Algorithmes génétiques hybrides en optimisation combinatoire." Phd thesis, Ecole normale supérieure de lyon - ENS LYON, 1999. http://tel.archives-ouvertes.fr/tel-00010950.
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Cette thèse aborde le problème de la résolution des problèmes combinatoires à l'aide d'algorithmes génétiques. Ce type d'algorithme présente en effet nombres d'avantages. Cependant, ils sont généralement relativement lents. Cette thèse est donc centrée sur les algorithmes hybrides, c'est-à-dire des algorithmes construits à l'aide de plusieurs méthodes différentes. Dans notre cas, nous étudions les algorithmes qui réunissent algorithmes génétiques et heuristiques. Il existe deux méthodes pour générer de tels algorithmes qui sont la représentation directe et la représentation indirecte. Ces deux méthodes sont étudiés au travers de trois problèmes distincts : l'ordonnancement statique de programmes parallèles, le placement de composants électroniques et la planification de réseaux cellulaires. Pour chacun des trois problèmes, les algorithmes hybrides ont montrés leur efficacité. Pour le problème de la planification de réseaux cellulaires, une nouvelle modélisation a été faite. Cette modélisation permet d'effectuer en même temps le placement des émetteurs et l'allocation de fréquences.
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Rebreyend, Pascal. "Algorithmes genetiques hybrides en optimisation combinatoire." Lyon, École normale supérieure (sciences), 1999. http://www.theses.fr/1999ENSL0108.
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Cette these porte sur les problemes d'optimisation combinatoire et sur leur resolution grace aux algorithmes genetiques, notamment ceux hybrides. Cette these traite les trois problemes suivants : l'ordonnancement de programmes paralleles, le placement de composants sur circuits imprimes et la construction de reseaux de telephonie cellulaire. Ces problemes sont resolus par l'utilisation d'algorithmes genetiques hybrides. Les algorithmes genetiques sont une methode interessante et facilement parallelisable pour trouver une solution sous-optimale d'un probleme combinatoire. Ils sont bases sur la theorie de l'evolution des especes. Leur methode consiste donc a faire evoluer une population d'individus ou de solutions. Cette these examine et compare les deux principales facons de coupler un algorithme genetique avec une heuristique. Ces deux methodes sont nommees representation directe et representation indirecte. Dans le cas de la representation directe, l'heuristique est introduite au sein de l'algorithme genetique en modifiant les operateurs de croisement ou de mutation. La representation indirecte consiste a utiliser l'algorithme genetique pour determiner un ordre total sur les elements du probleme. On utilise alors une heuristique ou algorithme de liste qui construit la solution pas a pas en tenant compte de cet ordre. A part le probleme de l'ordonnancement, cette these presente et explique la modelisation de chaque probleme, modelisation qui est necessaire afin de pouvoir exploiter au mieux les caracteristiques des algorithmes genetiques. Les trois problemes etudies ont permis d'experimenter les deux types d'algorithmes genetiques hybrides. Les algorithmes hybrides testes ont montre leur efficacite par rapport aux heuristiques classiques. Les resultats obtenus confirment l'interet d'adapter l'algorithme genetique au probleme traite.
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Vladimir, Lončar. "Hybrid parallel algorithms for solving nonlinear Schrödinger equation." Phd thesis, Univerzitet u Novom Sadu, Prirodno-matematički fakultet u Novom Sadu, 2017. https://www.cris.uns.ac.rs/record.jsf?recordId=104931&source=NDLTD&language=en.
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Numerical methods and algorithms for solving of partial differential equations, especially parallel algorithms, are an important research topic, given the very broad applicability range in all areas of science. Rapid advances of computer technology open up new possibilities for development of faster algorithms and numerical simulations of higher resolution. This is achieved through paralleliza-tion at different levels that practically all current computers support.In this thesis we develop parallel algorithms for solving one kind of partial differential equations known as nonlinear Schrödinger equation (NLSE) with a convolution integral kernel. Equations of this type arise in many fields of physics such as nonlinear optics, plasma physics and physics of ultracold atoms, as well as economics and quantitative finance. We focus on a special type of NLSE, the dipolar Gross-Pitaevskii equation (GPE), which characterizes the behavior of ultracold atoms in the state of Bose-Einstein condensation.We present novel parallel algorithms for numerically solving GPE for a wide range of modern parallel computing platforms, from shared memory systems and dedicated hardware accelerators in the form of graphics processing units (GPUs), to heterogeneous computer clusters. For shared memory systems, we provide an algorithm and implementation targeting multi-core processors us-ing OpenMP. We also extend the algorithm to GPUs using CUDA toolkit and combine the OpenMP and CUDA approaches into a hybrid, heterogeneous al-gorithm that is capable of utilizing all available resources on a single computer. Given the inherent memory limitation a single computer has, we develop a distributed memory algorithm based on Message Passing Interface (MPI) and previous shared memory approaches. To maximize the performance of hybrid implementations, we optimize the parameters governing the distribution of data and workload using a genetic algorithm. Visualization of the increased volume of output data, enabled by the efficiency of newly developed algorithms, represents a challenge in itself. To address this, we integrate the implementations with the state-of-the-art visualization tool (VisIt), and use it to study two use-cases which demonstrate how the developed programs can be applied to simulate real-world systems.Numerički metodi i algoritmi za rešavanje parcijalnih diferencijalnih jednačina, naročito paralelni algoritmi, predstavljaju izuzetno značajnu oblast istraživanja, uzimajući u obzir veoma široku primenljivost u svim oblastima nauke. Veliki napredak informacione tehnologije otvara nove mogućnosti za razvoj bržih al-goritama i numeričkih simulacija visoke rezolucije. Ovo se ostvaruje kroz para-lelizaciju na različitim nivoima koju poseduju praktično svi moderni računari. U ovoj tezi razvijeni su paralelni algoritmi za rešavanje jedne vrste parcijalnih diferencijalnih jednačina poznate kao nelinearna Šredingerova jednačina sa inte-gralnim konvolucionim kernelom. Jednačine ovog tipa se javljaju u raznim oblas-tima fizike poput nelinearne optike, fizike plazme i fizike ultrahladnih atoma, kao i u ekonomiji i kvantitativnim finansijama. Teza se bavi posebnim oblikom nelinearne Šredingerove jednačine, Gros-Pitaevski jednačinom sa dipol-dipol in-terakcionim članom, koja karakteriše ponašanje ultrahladnih atoma u stanju Boze-Ajnštajn kondenzacije.U tezi su predstavljeni novi paralelni algoritmi za numeričko rešavanje Gros-Pitaevski jednačine za širok spektar modernih računarskih platformi, od sis-tema sa deljenom memorijom i specijalizovanih hardverskih akceleratora u ob-liku grafičkih procesora, do heterogenih računarskih klastera. Za sisteme sa deljenom memorijom, razvijen je algoritam i implementacija namenjena više-jezgarnim centralnim procesorima korišćenjem OpenMP tehnologije. Ovaj al-goritam je proširen tako da radi i u okruženju grafičkih procesora korišćenjem CUDA alata, a takođe je razvijen i predstavljen hibridni, heterogeni algoritam koji kombinuje OpenMP i CUDA pristupe i koji je u stanju da iskoristi sve raspoložive resurse jednog računara.Imajući u vidu inherentna ograničenja raspoložive memorije koju pojedinačan računar poseduje, razvijen je i algoritam za sisteme sa distribuiranom memorijom zasnovan na Message Passing Interface tehnologiji i prethodnim algoritmima za sisteme sa deljenom memorijom. Da bi se maksimalizovale performanse razvijenih hibridnih implementacija, parametri koji određuju raspodelu podataka i računskog opterećenja su optimizovani korišćenjem genetskog algoritma. Poseban izazov je vizualizacija povećane količine izlaznih podataka, koji nastaju kao rezultat efikasnosti novorazvijenih algoritama. Ovo je u tezi rešeno kroz inte-graciju implementacija sa najsavremenijim alatom za vizualizaciju (VisIt), što je omogućilo proučavanje dva primera koji pokazuju kako razvijeni programi mogu da se iskoriste za simulacije realnih sistema.
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Collins, Joshua Stewart. "Rekernelisation Algorithms in Hybrid Phylogenies." Thesis, University of Canterbury. Mathematics and Statistics, 2009. http://hdl.handle.net/10092/2852.
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It has become well known that an evolutionary tree is inadequate to represent fully the history of life. Two possible ways of dealing with this are the rooted subtree prune and regraft distance between a pair of trees, which measures how different they are, and the slightly more biologically sound hybridisation number of a set of trees that attempts to determine the minimum number of hybrid events that must have occurred for a given set of evolutionary trees. When characterised via agreement forests both problems are, although NP hard, fixed parameter tractable---meaning the problem can be converted to a similar problem with a smaller input size. This thesis investigates ways of improving existing algorithms for calculating the minimum rooted subtree prune and regraft distance and hybridisation number for a pair or, in the latter case, set of trees. In both cases a technique is used that allows the problem to be rekernelised during the run of the program. Another, less effective method, is also looked at which finds the rooted subtree prune and regraft distance or hybridisation number solely on what cannot be contained within any agreement forest. Additionally the characterisation of the minimum rooted subtree prune and regraft distance via maximum agreement forests is extended to non-binary trees and the hybridisation number of a set of phylogenetic trees is extended to unrooted trees.
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Etancelin, Jean-Matthieu. "Couplage de modèles, algorithmes multi-échelles et calcul hybride." Thesis, Grenoble, 2014. http://www.theses.fr/2014GRENM072/document.
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Dans cette thèse nous explorons les possibilités offertes par l'implémentation de méthodes hybrides sur des machines de calcul hétérogènes dans le but de réaliser des simulations numériques de problèmes multiéchelles. La méthode hybride consiste à coupler des méthodes de diverses natures pour résoudre les différents aspects physiques et numériques des problèmes considérés. Elle repose sur une méthode particulaire avec remaillage qui combine les avantages des méthodes Lagrangiennes et Eulériennes. Les particules sont déplacées selon le champ de vitesse puis remaillées à chaque itération sur une grille en utilisant des formules de remaillage d'ordre élevés. Cette méthode semi-Lagrangienne bénéficie des avantages du maillage régulier mais n'est pas contrainte par une condition de CFL.Nous construisons une classe de méthodes d'ordre élevé pour lesquelles les preuves de convergence sont obtenues sous la seule contrainte de stabilité telle que les trajectoires des particules ne se croisent pas.Dans un contexte de calcul à haute performances, le développement du code de calcul a été axé sur la portabilité afin de supporter l'évolution rapide des architectures et leur nature hétérogène. Une étude des performances numériques de l'implémentation GPU de la méthode pour la résolution d'équations de transport est réalisée puis étendue au cas multi-GPU. La méthode hybride est appliquée à la simulation du transport d'un scalaire passif dans un écoulement turbulent 3D. Les deux sous-problèmes que sont l'écoulement turbulent et le transport du scalaire sont résolus simultanément sur des architectures multi-CPU et multi-GPUIn this work, we investigate the implementation of hybrid methods on heterogeneous computers in order to achieve numerical simulations of multi-scale problems. The hybrid numerical method consists of coupling methods of different natures to solve the physical and numerical characteristics of the problem. It is based on a remeshed particle method that combines the advantages of Lagrangian and Eulerian methods. Particles are pushed by local velocities and remeshed at every time-step on a grid using high order interpolation formulas. This forward semi-lagrangian method takes advantage of the regular mesh on which particles are reinitialized but is not limited by CFL conditions.We derive a class of high order methods for which we are able to prove convergence results under the sole stability constraint that particle trajectories do not intersect.In the context of high performance computing, a strong portability constraint is applied to the code development in order to handle the rapid evolution of architectures and their heterogeneous nature. An analysis of the numerical efficiency of the GPU implementation of the method is performed and extended to multi-GPU platforms. The hybrid method is applied to the simulation of the transport of a passive scalar in a 3D turbulent flow. The two sub-problems of the flow and the scalar calculations are solved simultaneously on multi-CPU and multi-GPU architectures
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Bachelet, Vincent. "Métaheuristiques parallèles hybrides : application au problème d'affection quadratique." Lille 1, 1999. https://pepite-depot.univ-lille.fr/LIBRE/Th_Num/1999/50376-1999-397.pdf.
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Ce mémoire présente une étude sur la conception de méthodes hybrides efficaces pour l'optimisation combinatoire. Nous avons mené cette étude sur trois fronts : - la structure intrinsèque des instances du QAP (problème d'affectation quadratique) ; - les métaheuristiques sur environnements distribués ; - les mécanismes d'hybridation et de coévolution. Pour analyser les instances, nous avons étudié leurs paysages de fitness. Nous avons adopté une démarche basée sur le comportement d'une méthode de descente et avons proposé des indicateurs qui font ressortir trois tendances : type I - un paysage plat et rugueux ; type II - regroupement central des optima locaux constituant un massif ; type III - plusieurs massifs d'optima locaux éparpillés. Cette taxinomie originale rejoint d'autres classements obtenus de manière empirique. Pour étudier les métaheuristiques parallèles, nous avons distingué les recherches locales des méthodes à population. Pour les deux cas, nous avons proposé un modèle et avons sélectionné différentes formes de parallélisation. Pour les exécutions, nous avons utilisé diverses plates-formes parallèles. Nous avons constaté que les recherches locales sont plus efficaces sur les instances uniformes (type I) et qu'à l'inverse, les méthode à population sont plus performantes sur les instances structurées (type II). Ces constatations nous ont amené à considérer l'hybridation pour résoudre les instances de type III. Dans notre présentation des métaheuristiques hybrides, outre une taxinomie originale, nous avons proposé une méthode hybride parallèle qui associe puissance de calcul et coévolution. Cet hybride repose sur la coévolution d'agents de recherche locale, de diversification, et d'intensification. Ces agents coopèrent à travers une mémoire adaptative. Nous avons appliqué ce modèle coévolutionniste au QAP, et avons égalé, pour de nombreuses instances du QAP, les meilleurs résultats connus.
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Abdelkafi, Omar. "Métaheuristiques hybrides distribuées et massivement parallèles." Thesis, Mulhouse, 2016. http://www.theses.fr/2016MULH9578/document.
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De nombreux problèmes d'optimisation propres à différents secteurs industriels et académiques (énergie, chimie, transport, etc.) nécessitent de concevoir des méthodes de plus en plus efficaces pour les résoudre. Afin de répondre à ces besoins, l'objectif de cette thèse est de développer une bibliothèque composée de plusieurs métaheuristiques hybrides distribuées et massivement parallèles. Dans un premier temps, nous avons étudié le problème du voyageur de commerce et sa résolution par la méthode colonie de fourmis afin de mettre en place les techniques d'hybridation et de parallélisation. Ensuite, deux autres problèmes d'optimisation ont été traités, à savoir, le problème d'affectation quadratique (QAP) et le problème de la résolution structurale des zéolithes (ZSP). Pour le QAP, plusieurs variantes basées sur une recherche taboue itérative avec des diversifications adaptatives ont été proposées. Le but de ces propositions est d'étudier l'impact de : l'échange des données, des stratégies de diversification et des méthodes de coopération. Notre meilleure variante est comparée à six des meilleurs travaux de la littérature. En ce qui concerne le ZSP, deux nouvelles formulations de la fonction objective sont proposées pour évaluer le potentiel des structures zéolitiques trouvées. Ces formulations sont basées sur le principe de pénalisation et de récompense. Deux algorithmes génétiques hybrides et parallèles sont proposés pour générer des structures zéolitiques stables. Nos algorithmes ont généré actuellement six topologies stables, parmi lesquelles trois ne sont pas répertoriées sur le site Web du SC-IZA ou dans l'Atlas of Prospective Zeolite StructuresMany optimization problems specific to different industrial and academic sectors (energy, chemicals, transportation, etc.) require the development of more effective methods in resolving. To meet these needs, the aim of this thesis is to develop a library of several hybrid metaheuristics distributed and massively parallel. First, we studied the traveling salesman problem and its resolution by the ant colony method to establish hybridization and parallelization techniques. Two other optimization problems have been dealt, which are, the quadratic assignment problem (QAP) and the zeolite structure problem (ZSP). For the QAP, several variants based on an iterative tabu search with adaptive diversification have been proposed. The aim of these proposals is to study the impact of: the data exchange, the diversification strategies and the methods of cooperation. Our best variant is compared with six from the leading works of the literature. For the ZSP two new formulations of the objective function are proposed to evaluate the potential of the zeolites structures founded. These formulations are based on reward and penalty evaluation. Two hybrid and parallel genetic algorithms are proposed to generate stable zeolites structures. Our algorithms have now generated six stable topologies, three of them are not listed in the SC-JZA website or in the Atlas of Prospective Zeolite Structures
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Guimarans, Serrano Daniel. "Hybrid algorithms for solving routing problems." Doctoral thesis, Universitat Autònoma de Barcelona, 2012. http://hdl.handle.net/10803/96386.
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Un component important de molts sistemes de distribució és el càlcul de les rutes dels vehicles per servir els clients. El Vehicle Routing Problem (VRP) proporciona el marc teòric per tractar aquest tipus de problemes logístics relacionats amb la distribució física de béns. Per la seva complexitat i aplicabilitat, aquest tipus de problemes logístics es troba entre les línies de recerca més populars en optimització combinatòria.
Aquesta tesi de doctorat té com a objectiu la introducció de tres metodologies diferents per resoldre el VRP. Aquestes metodologies han estat especialment dissenyades per ser flexibles, en el sentit que poden ser utilitzades, amb adaptacions menors, per resoldre diferents variants del VRP presents en casos d’aplicació industrial.
En les tres metodologies descrites en aquest treball s’utilitzen diferents tècniques per aconseguir la flexibilitat, l’eficiència i la robustesa desitjades. Constraint Programming (CP) ha estat escollit com a paradigma de modelat per descriure les principals restriccions presents en el VRP. CP proporciona la flexibilitat desitjada per les tres metodologies, atès que afegir restriccions addicionals presents en molts casos d’aplicació real només afecta al modelat del problema, però no a la definició dels algorismes de cerca. En les dues primeres metodologies descrites, el model de CP només s’utilitza per comprovar la factibilitat de les solucions durant la cerca. La tercera metodologia presenta un model més ric del VRP que permet tractar diferents variants del problema. En aquest cas, la cerca es realitza i es controla fent servir els mecanismes que CP proporciona.
La Relaxació Lagrangiana (LR) i una versió probabilística de l’heurística Clarke and Wright Savings (RCWS) s’utilitzen amb una finalitat molt específica dins de les metodologies. LR s’utilitza per minimitzar la distància total recorreguda pels vehicles, mentre que la RCWS es fa servir per calcular una solució inicial de bona qualitat. Ambdós mètodes proporcionen una aproximació eficient als problemes respectius. A més, la utilització de LR permet reduir la complexitat computacional dels processos de cerca local i, d’aquesta manera, redueix el temps computacional necessari per resoldre el VRP.
Totes les metodologies es basen en la metaheurística coneguda com Variable Neighborhood Search (VNS). El VNS està format per una família d’algorismes que aprofiten sistemàticament la idea de canviar el veïnat explorat al voltant d’una solució, tant en el procés de cerca per trobar un mínim local com en el procés de pertorbació, per escapar de la vall corresponent. Malgrat que és un mètode bastant estès, hi ha pocs exemples d’aplicació en el VRP. En tot cas, fins i tot els mètodes VNS més simples han aconseguit bons resultats quan han estat aplicats en aquest problema.
Aquesta tesi té com a objectiu contribuir en la recerca de l’aplicació de la metaheurística VNS en el VRP. Aquesta ha estat escollida com a marc en el que integrar les tècniques mencionades. Així, la metaheurística s’utilitza per guiar la cerca, mentre que l’eficiència desitjada s’aconsegueix mitjançant els mètodes que s’hi integren. D’altra banda, utilitzar CP com a paradigma de modelat proporciona la flexibilitat requerida. Aquesta característica és especialment rellevant en el cas de la darrera metodologia descrita. En aquest cas, la cerca de CP es guia mitjançant una combinació de les metaheurístiques VNS i Large Neighborhood Search (LNS). Aquesta metodologia representa una primera aproximació a la resolució eficient de problemes VRP més complexos, similars a casos d’aplicació real.An important component of many distribution systems is routing vehicles to serve customers. The Vehicle Routing Problem (VRP) provides a theoretical framework for approaching this class of logistics problems dealing with physical distribution. Because of its complexity and applicability, this class of logistics problems is among the most popular research areas in combinatorial optimization. This PhD. Thesis is aimed to introduce three different yet related hybrid methodologies to solve the VRP. These methodologies have been especially designed for being flexible in the sense that they can be used, with minor adaptations, for solving different variants of the VRP present in industrial application cases. In the three methodologies described in this work, different technologies are used to achieve the desired flexibility, efficiency, and robustness. Constraint Programming (CP) has been chosen as the modeling paradigm to describe the main constraints involved in the VRP. CP provides the pursued flexibility for the three methodologies, since adding side constraints present in most real application cases becomes a modeling issue and does not affect the search algorithm definition. In the first two hybrid methodologies, the CP model is used to check solution's feasibility during search. The third methodology presents a richer model for the VRP capable of tackling different problem variants. In this case, the search is performed and controlled from a CP perspective. Lagrangian Relaxation (LR) and a probabilistic version of the Clarke and Wright Savings (CWS) heuristic are used for specific purposes within the proposed methodologies. The former is used for minimizing the total traveled distance and the latter to provide a good initial solution. Both methods provide an efficient approach to the respectively faced problems. Moreover, the use of LR permits reducing the computational complexity of the performed local search processes and therefore reduces the required computational time to solve the VRP. All methodologies are based on the so-called Variable Neighborhood Search (VNS) metaheuristic. The VNS is formed by a family of algorithms that exploits systematically the idea of neighborhood changes both in the search phase to find a local minimum, and in perturbation phase, to escape from the corresponding valley. Although it is an extended method, there are few examples of its application to the VRP. However, interesting results have been obtained even applying the simplest VNS algorithms to this problem. The present thesis is aimed to contribute to the current research on the application of the VNS metaheuristic to the VRP. It has been chosen as the framework where the mentioned techniques are embedded. Hence, the metaheuristic is used to guide the search, while the desired efficiency is provided by the composing methods. On the other hand, using CP as the modeling paradigm provides the required flexibility. This characteristic is enhanced in the last described methodology. In this case, the CP search is guided by a combination of the VNS and the Large Neighborhood Search (LNS) metaheuristics. This methodology represents an initial approach for tackling efficiently more complex and richer VRP, similar to real application cases.
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Lee, David Alexander James. "Hybrid algorithms for distributed constraint satisfaction." Thesis, Robert Gordon University, 2010. http://hdl.handle.net/10059/509.
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A Distributed Constraint Satisfaction Problem (DisCSP) is a CSP which is divided into several inter-related complex local problems, each assigned to a different agent. Thus, each agent has knowledge of the variables and corresponding domains of its local problem together with the constraints relating its own variables (intra-agent constraints) and the constraints linking its local problem to other local problems (inter-agent constraints). DisCSPs have a variety of practical applications including, for example, meeting scheduling and sensor networks. Existing approaches to Distributed Constraint Satisfaction can be mainly classified into two families of algorithms: systematic search and local search. Systematic search algorithms are complete but may take exponential time. Local search algorithms often converge quicker to a solution for large problems but are incomplete. Problem solving could be improved through using hybrid algorithms combining the completeness of systematic search with the speed of local search. This thesis explores hybrid (systematic + local search) algorithms which cooperate to solve DisCSPs. Three new hybrid approaches which combine both systematic and local search for Distributed Constraint Satisfaction are presented: (i) DisHyb; (ii) Multi-Hyb and; (iii) Multi-HDCS. These approaches use distributed local search to gather information about difficult variables and best values in the problem. Distributed systematic search is run with a variable and value ordering determined by the knowledge learnt through local search. Two implementations of each of the three approaches are presented: (i) using penalties as the distributed local search strategy and; (ii) using breakout as the distributed local search strategy. The three approaches are evaluated on several problem classes. The empirical evaluation shows these distributed hybrid approaches to significantly outperform both systematic and local search DisCSP algorithms. DisHyb, Multi-Hyb and Multi-HDCS are shown to substantially speed-up distributed problem solving with distributed systematic search taking less time to run by using the information learnt by distributed local search. As a consequence, larger problems can now be solved in a more practical timeframe.
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Yu, Chia Woo. "Improved algorithms for hybrid video coding." Thesis, University of Warwick, 2007. http://wrap.warwick.ac.uk/3841/.
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Doerner, Karl, Richard F. Hartl, and Marc Reimann. "A hybrid ACO algorithm for the full truckload transportation problem." SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, 2001. http://epub.wu.ac.at/74/1/document.pdf.
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In this paper we propose a hybrid ACO approach to solve a full truckload transportation problem. Hybridization is achieved through the use of a problem specific heuristic. This heuristic is utilized both, to initialize the pheromone information and to construct solutions in the ACO pro-cedure. The main idea is to use information about the required fleetsize, by initializing the system with a number of vehicles rather than opening vehicles one at a time as needed. Our results show the advantages of this new approach over more traditional, i.e. sequential, approaches.Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"
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Nieländer, N. Ulf. "CHEOPS: Das Chemnitzer hybrid-evolutionäre Optimierungssystem." Doctoral thesis, Universitätsbibliothek Chemnitz, 2009. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-200901000.
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Evolutionäre Algorithmen übertragen den natürlich-biologischen Evolutionsprozess auf die Lösung mathematischer, techn(olog)ischer oder ökonomischer Optimierungsprobleme aus Forschung, Industrie und Wirtschaft. Die als Vorbild dienenden Prinzipien und Mechanismen werden jedoch nicht direkt kopiert, sondern lediglich ihre Wirkungen abstrakt imitiert sowie algorithmisch implementiert, um dann für die jeweilige Aufgabenstellung immer bessere Individuen mittels Computer im Zeitraffer heranzuzüchten und schließlich (fast-)optimale Lösungspunkte aufzufinden. Dabei bedarf es keiner expliziten Richtungsinformation oder sonstiger Wegweiser, um die Züchtung bzw. Suche zielgerichtet zu dirigieren und dann erfolgreich zu konvergieren. Sukzessive orientieren sich Evolutionäre Algorithmen allein anhand von Lösungspunkt-Zielfunktionswert-Paaren, also am Erfolg oder Misserfolg bereits durchgeführter Suchschritte. Aufgrund dieser konzeptuellen Anspruchslosigkeit haben sie dem Selektionsdruck der Praxis standgehalten, viele Anwendungsgebiete erobert und sich als universell einsetzbare Lösungsverfahren / Optimierungswerkzeuge etabliert.
Das für diese Dissertation entwickelte und hier eingehend dokumentierte Chemnitzer hybrid-evolutionäre Optimierungssystem CHEOPS ist konzipiert als leistungsstarker, universeller, anpassungsfähiger und erweiterbarer Evolutionärer Algorithmus zur statischen Parameteroptimierung deterministischer Probleme. Als numerischer Benchmark zur empirischen Beurteilung von Erfolgswahrscheinlichkeit und Fortschrittsgeschwindigkeit ist außerdem eine ausgewogene Schar schwierig zu optimierender mathematischer Testfunktionen zusammengestellt. Dabei sind sie bewusst so konstruiert, dass sie derartige charakteristische Merkmale besitzen, die auch bei praxisrelevanten Optimierungsproblemen oft vorliegen oder zu erwarten sind. Verschiedene topologische Funktionseigenschaften haben tatsächliche oder auch nur vermeintliche Schwierigkeiten bei der Optimierung besonders verdeutlicht. CHEOPS beinhaltet eine Vielzahl an Werkzeugen und Funktion(alität)en, wird aber weiterentwickelt hinsichtlich mehrkriterieller Optimierung sowie hybrider Optimierung als Themengebiete für zukünftige Herausforderungen.
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Smaoui, Salma. "Réseaux possibilistes hybrides : représentation des interventions et algorithmes." Artois, 2007. http://www.theses.fr/2007ARTO0407.
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Cette thèse a pour objectif d’étudier la notion de causalité et de développer les algorithmes de propagation dans le cadre des réseaux possibilistes. Nous proposons d’introduire l’opérateur do comme un moyen de représentation des interventions dans les réseaux possibilistes. Nous montrons aussi que l’utilisation de cet opérateur dans le cadre possibiliste présente des avantages par rapport à son application aux réseaux bayésiens probabilistes notamment au niveau de l’efficacité de la propagation de telles informations sur le reste du système. Un modèle causal possibiliste basé sur les réseaux causaux possibilistes et permettant d’intégrer les interventions est mis en place. Ce modèle permet de définir une typologie des différentes relations causales existantes. Le traitement des observations et la gestion des interventions sont unifiés à travers le processus de conditionnement et de propagation. De ce fait, nous proposons d’améliorer les algorithmes de propagation tout en tirant profit des avantages offerts par le cadre possibiliste. Notre approche consiste à combiner les deux outils de représentation dans le cadre possibiliste : la logique possibiliste et les réseaux possibilistes. Ces alternatives constituent toutes les deux des représentations compactes des distributions de possibilité. Ainsi, l’incertitude au niveau de la nouvelle représentation, dite hybride, des réseaux possibilistes est quantifiée localement en termes de bases de connaissances possibilistes à la place des distributions de possibilité. Un algorithme de propagation adapté à la nouvelle représentation est proposé. L’étude expérimentale confirme les apports de ce nouvel algorithmeThis thesis aims to study the causality notion and to develop propagation algorithms in the possibilistic networks framework. Intervention is a crucial notion to insure an efficient causal analysis in the sense that it facilitates causality ascriptions. We propose to introduce the do operator to represent interventions in possibilistic networks. We show that using such operator in the possibilistic framework reveals, in some cases, more interesting than its application for Bayesian networks especially for the propagation efficiency when reasoning with interventions. A possibilistic causal model based on possibilistic networks and allowing handling interventions is provided. This model proposes a typology of the forms that causal relationships can take in the possibilistic framework. Handling observation and dealing with interventions are unified through the propagation and conditioning process. We propose to improve propagation algorithms by taking advantages of possibility theory. Our approach consists of combining two representation tools in the possibility framework: possibilistic logic and possibilistic networks. These two alternatives both provide compact representations of possibility distributions. Hence, uncertainty at the level of nodes is represented in terms of possibilistic knowledge bases instead of possibility distributions. A propagation algorithm using the new representation, called hybrid, is proposed. Experimental results confirm the contribution of this new algorithm
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Ediger, David. "Analyzing hybrid architectures for massively parallel graph analysis." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/47659.
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The quantity of rich, semi-structured data generated by sensor networks, scientific simulation, business activity, and the Internet grows daily. The objective of this research is to investigate architectural requirements for emerging applications in massive graph analysis. Using emerging hybrid systems, we will map applications to architectures and close the loop between software and hardware design in this application space. Parallel algorithms and specialized machine architectures are necessary to handle the immense size and rate of change of today's graph data. To highlight the impact of this work, we describe a number of relevant application areas ranging from biology to business and cybersecurity. With several proposed architectures for massively parallel graph analysis, we investigate the interplay of hardware, algorithm, data, and programming model through real-world experiments and simulations. We demonstrate techniques for obtaining parallel scaling on multithreaded systems using graph algorithms that are orders of magnitude faster and larger than the state of the art. The outcome of this work is a proposed hybrid architecture for massive-scale analytics that leverages key aspects of data-parallel and highly multithreaded systems. In simulations, the hybrid systems incorporating a mix of multithreaded, shared memory systems and solid state disks performed up to twice as fast as either homogeneous system alone on graphs with as many as 18 trillion edges.
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El-Mihoub, Tarek A. "New hybrid genetic algorithms for parameter search." Thesis, Nottingham Trent University, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.442088.
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Mapfaira, Herbert. "Assembly line balancing using hybrid genetic algorithms." Thesis, University of Nottingham, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.268500.
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Khan, Wali. "Hybrid multiobjective evolutionary algorithms based on decomposition." Thesis, University of Essex, 2012. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.549297.
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Rubio, Juan Carlos Campos. "Projeto, construção e avaliação de microposicionadores para usinagem de ultraprecisão." Universidade de São Paulo, 2000. http://www.teses.usp.br/teses/disponiveis/18/18135/tde-11072018-153521/.
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De maneira geral, a necessidade de aumentar o desempenho e diminuir o tamanho dos sistemas mecatrônicos tem levado a indústria moderna a idealizar e desenvolver sistemas de posicionamento com boas características de aceleração e precisão de posicionamento. Por outro lado, a crescente demanda de componentes com melhores características metrológicas e de acabamento, tais corno lentes para raio X e infra vermelho, tem exigido o desenvolvimento de variados tipos de sistemas de microposicionamento capazes de movimentar elementos de máquinas em distâncias muito pequenas com alto grau de exatidão, dentre os quais podem-se destacar os acionados por meio de atuadores piezoelétricos. Este trabalho propõe a utilização de um novo tipo de atuador baseado na propriedade de estricção eletromagnética de certas ligas metálicas (atuadores magnetoestritivos) associado a um sistema de controle digital que utiliza um algoritmo de controle baseado em lógica difusa e redes neurais artificiais para o controle de microposicionamento. Metodologias e princípios de projeto para engenharia de precisão são abordados de forma a auxiliar no desenvolvimento de dois protótipos de posicionadores para uso em usinagem de ultraprecisão. Resultados obtidos em testes experimentais apontam para urna melhoria no comportamento dinâmico dos microposicionadores acionados por atuadores magnetoestritivos. Isto permite sua utilização como alternativa válida no posicionamento submicrométrico.In general, actual requirements such as high performance and small sizes of mechatronic systems, has led modern industry to design positioning systems with good characteristics of acceleration and positioning accuracy. The increasing demand of components with better metrological and finish characteristics, as X-ray and infra-red lens, has allowed the development of a number of types of micropositioning systems that are able to move machine elements to very small distances with high levels of accuracy. In this work it is proposed the use of a new type of actuator that applies the properties of electromagnetic strain of certain metallic alloys (magnetostrictive actuators). lt is also proposed the application of a digital control system that uses a control algorithm which is based on fuzzy logic and artificial neural networks for the micropositioning control. Design principles and methodologies related to precision engineering are discussed with the purpose of aiding the development of two prototypes of positioners for ultraprecision rnachining, experimental results show that micropositioner driven by magnetostrictive actuators have better dynamics behaviours. This allows the use of such actuators as an valid alternative for positioning in submicrometer range.
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Karray, Asma. "Contribution à l’ordonnancement d’ateliers agroalimentaires utilisant des méthodes d’optimisation hybrides." Thesis, Ecole centrale de Lille, 2011. http://www.theses.fr/2011ECLI0024/document.
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Nos travaux concernent la mise en œuvre de méthodologies pour la résolution de problèmes d’ordonnancement en industries agroalimentaires. Trois nouvelles approches basées sur les algorithmes génétiques, sont proposées pour la résolution de problèmes d’ordonnancement multi-objectifs : les algorithmes génétiques séquentiels (SGA), les algorithmes génétiques parallèles (PGA) et les algorithmes génétiques parallèles séquentiels (PSGA). Deux approches coopératives multi-objectifs en mode relais, SH_GA/TS et SH_GA/SA, hybridant toutes les deux des métaheuristiques de haut niveau, sont par la suite proposées. Un algorithme évolutionnaire et un algorithme de recherche locale sont, dans ce cas exécutés séquentiellementThe purpose of our works is the implementation of methodologies for the resolution of the agro-food industry scheduling problem. Three new approaches based on genetic algorithms are proposed to solve multi-objectives scheduling problems: sequential genetic algorithms (SGA), parallel genetic algorithms (PGA) and parallel sequential genetic algorithms (PSGA). Two high-level hybrid algorithms, SH_GA/TS et SH_GA/SA, are also proposed. The purpose in this hybridization is to benefit the exploration of the solution space by a population of individuals with the exploitation of solutions through a smart search of the local search algorithm
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Shen, Gang. "Shadow Price Guided Genetic Algorithms." Digital Archive @ GSU, 2012. http://digitalarchive.gsu.edu/cs_diss/64.
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The Genetic Algorithm (GA) is a popular global search algorithm. Although it has been used successfully in many fields, there are still performance challenges that prevent GA’s further success. The performance challenges include: difficult to reach optimal solutions for complex problems and take a very long time to solve difficult problems. This dissertation is to research new ways to improve GA’s performance on solution quality and convergence speed. The main focus is to present the concept of shadow price and propose a two-measurement GA. The new algorithm uses the fitness value to measure solutions and shadow price to evaluate components. New shadow price Guided operators are used to achieve good measurable evolutions. Simulation results have shown that the new shadow price Guided genetic algorithm (SGA) is effective in terms of performance and efficient in terms of speed.
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Hinnenthal, Kristian [Verfasser]. "Models and algorithms for hybrid networks and hybrid programmable matter / Kristian Hinnenthal." Paderborn : Universitätsbibliothek, 2021. http://d-nb.info/1241183112/34.
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Idrissi, Aouad Maha. "Conception d'algorithmes hybrides pour l'optimisation de l'énergie mémoire dans les systèmes embarqués et de fonctions multimodales." Thesis, Nancy 1, 2011. http://www.theses.fr/2011NAN10029/document.
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La mémoire est considérée comme étant gloutonne en consommation d'énergie, un problème sensible, particulièrement dans les systèmes embarqués. L'optimisation globale de fonctions multimodales est également un problème délicat à résoudre du fait de la grande quantité d'optima locaux de ces fonctions. Dans ce mémoire, je présente différents nouveaux algorithmes hybrides et distribués afin de résoudre ces deux problèmes d'optimisation. Ces algorithmes sont comparés avec les méthodes classiques utilisées dans la littérature et les résultats obtenus sont encourageants. En effet, ces résultats montrent une réduction de la consommation d'énergie en mémoire d'environ 76% jusqu'à plus de 98% sur nos programmes tests, d'une part. D'autre part, dans le cas de l'optimisation globale de fonctions multimodales, nos algorithmes hybrides convergent plus souvent vers la solution optimale globale. Des versions distribuées et coopératives de ces nouveaux algorithmes hybrides sont également proposées. Elles sont, par ailleurs, plus rapides que leurs versions séquentielles respectivesRésumé en anglais : Memory is considered to be greedy in energy consumption, a sensitive issue, especially in embedded systems. The global optimization of multimodal functions is also a difficult problem because of the large number of local optima of these functions. In this thesis report, I present various new hybrid and distributed algorithms to solve these two optimization problems. These algorithms are compared with conventional methods used in the literature and the results obtained are encouraging. Indeed, these results show a reduction in memory energy consumption by about 76% to more than 98% on our benchmarks on one hand. On the other hand, in the case of global optimization of multimodal functions, our hybrid algorithms converge more often to the global optimum solution. Distributed and cooperative versions of these new hybrid algorithms are also proposed. They are more faster than their respective sequential versions
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Kabalan, Bilal. "Systematic methodology for generation and design of hybrid vehicle powertrains." Thesis, Lyon, 2020. http://www.theses.fr/2020LYSE1048.
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Pour répondre aux objectifs de consommation des flottes de véhicules, au normes d’émissions de polluants et aux nouvelles demandes de l’usager, les constructeurs automobiles doivent développer des motorisations hybrides et électriques. Réaliser une chaine de traction hybride reste cependant une tâche difficile. Ces systèmes sont complexes et possèdent de nombreuses variables réparties sur différents niveaux : architecture, technologie des composants, dimensionnement et contrôle/commande. L’industrie manque encore d’environnements et d’outils pouvant aider à l’exploration de l’ensemble de l’espace de dimensionnement et à trouver la meilleure solution parmi tous ces niveaux. Cette thèse propose une méthodologie systématique pour répondre au moins partiellement à ce besoin. Partant d’un ensemble de composants, cette méthodologie permet de générer automatiquement tous les graphes d’architectures possibles en utilisant la technique de programmation par contraintes. Une représentation dédiée est développée pour visualiser ces graphes. Les éléments de boites de vitesse (embrayages, synchroniseurs) sont représentés avec un niveau de détails approprié pour générer de nouvelles transmission mécaniques sans trop complexifier le problème. Les graphes obtenus sont ensuite transformés en d’autres types de représentation : 0ABC Table (décrivant les connections mécaniques entre les composants), Modes Table (décrivant les modes de fonctionnement disponibles dans les architectures) et Modes Table + (décrivant pour chaque mode le rendement et le rapport de réduction global des chemins de transfert de l’énergie entre tous les composants). Sur la base de cette représentation, les nombreuses architectures générées sont filtrées et seules les plus prometteuses sont sélectionnées. Elles sont ensuite automatiquement évaluées et optimisées avec un modèle général spécifiquement développé pour calculer les performances et la consommation de toute les architectures générées. Ce modèle est inséré dans un processus d’optimisation à deux niveaux ; un algorithme génétique GA est utilisé pour le dimensionnement des composants et la programmation dynamique est utilisée au niveau contrôle (gestion de l’énergie) du système. Un cas d’étude est ensuite réalisé pour montrer le potentiel de cette méthodologie. Nous générons ainsi automatiquement toutes les architectures qui incluent un ensemble de composants défini à l’avance, et le filtrage automatique élimine les architectures présupposées non efficaces et sélectionnent les plus prometteuses pour l’optimisation. Les résultats montrent que la méthodologie proposée permet d’aboutir à une architecture meilleure (consommation diminuée de 5%) que celles imaginées de prime abord (en dehors de toute méthodologie)To meet the vehicle fleet-wide average CO2 targets, the stringent pollutant emissions standards, and the clients’ new demands, the automakers realized the inevitable need to offer more hybrid and electric powertrains. Designing a hybrid powertrain remains however a complex task. It is an intricate system involving numerous variables that are spread over different levels: architecture, component technologies, sizing, and control. The industry lacks frameworks or tools that help in exploring the entire design space and in finding the global optimal solution on all these levels. This thesis proposes a systematic methodology that tries to answer a part of this need. Starting from a set of chosen components, the methodology automatically generates all the possible graphs of architectures using constraint-programming techniques. A tailored representation is developed to picture these graphs. The gearbox elements (clutches, synchronizer units) are represented with a level of details appropriate to generate the new-trend dedicated hybrid gearboxes, without making the problem too complex. The graphs are then transformed into other types of representation: 0ABC Table (describing the mechanical connections between the components), Modes Table (describing the available modes in the architectures) and Modes Table + (describing for each available mode the global efficiency and ratio of the power flow between all the components). Based on these representations, the architectures are filtered and the most promising ones are selected. They are automatically assessed and optimized using a general hybrid model specifically developed to calculate the performance and fuel consumption of all the generated architectures. This model is inserted inside a bi-level optimization process: Genetic Algorithm GA is used on the sizing and components level, while Dynamic Programming DP is used on the control level. A case study is performed and the capability of the methodology is proven. It succeeded in automatically generating all the graphs of possible architectures, and filtering dismissed architectures that were then proven not efficient. It also selected the most promising architectures for optimization. The results show that the proposed methodology succeeded in finding an architecture better than the ones proposed without the methodology (consumption about 5% lower)
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Shaikh, Mohammad Shahid. "Optimal control of hybrid systems : theory and algorithms." Thesis, McGill University, 2004. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=85095.
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Many complex systems are hybrid in the sense that: (i) the state set possesses continuous and discrete components, and (ii) system evolution may occur in both continuous and discrete time. One important class of hybrid systems is that characterized by a feedback configuration of a set of continuous controlled low level systems and a high level discrete controller; such systems appear frequently in engineering and are particularly evident when a system is required to operate in a number of distinct modes. Other classes of hybrid systems are found in such diverse areas as (i) air traffic management systems, (ii) chemical process control, (iii) automotive engine-transmission systems, and (iv) intelligent vehicle-highway systems.In this thesis we first formulate a class of hybrid optimal control problems (HOCPs) for systems with controlled and autonomous location transitions and then present necessary conditions for hybrid system trajectory optimality. These necessary conditions constitute generalizations of the standard Minimum Principle (MP) and are presented for the cases of open bounded control value sets and compact control value sets. These conditions give information about the behaviour of the Hamiltonian and the adjoint process at both autonomous and controlled switching times.
Such proofs of the necessary conditions for hybrid systems optimality which can be found in the literature are sufficiently complex that they are difficult to verify and use; in contrast, the formulation of the HOCP given in Chapter 2 of this thesis, together with the use of (i) classical variational methods and more recent needle variation techniques, and (ii) a local controllability condition, called the small time tubular fountain (STTF) condition, make the proofs in that chapter comparatively accessible. We note that the STTF condition is used to establish the adjoint and Hamiltonian jump conditions in the autonomous switchings case.
A hybrid Dynamic Programming Principle (HDPP) generalizing the standard dynamic programming principle to hybrid systems is also derived and this leads to hybrid Hamilton-Jacobi-Bellman (HJB) equation which is then used to establish a verification theorem within this framework. (Abstract shortened by UMI.)
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Moursli, Omar. "Scheduling the hybrid flowshop : branch and bounnd algorithms." Université catholique de Louvain, 1999. http://edoc.bib.ucl.ac.be:81/ETD-db/collection/available/BelnUcetd-11262003-101952/.
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This thesis studies Production Scheduling in a multistage hybrid flowshop facility. It first states the general Production Planning and Scheduling problem and highlights some drawbacks of classical solutions. A theoretical decomposition-based approach is introduced whose main issue is to overcome non-efficient capacity utilization. By using Branch and Bound methods, an in-depth analysis of the scheduling part of the system is then carried out throughout the study and development of upper and lower bounds as well as branching schemes. Already-existing and new heuristics are presented and compared on different shop floor configurations. Five different heuristic approaches are studied. By scheduling the HFS one stage at a time the first approach uses different stage sequencing orders. The second and third approaches are mainly list heuristics. The second approach uses ideas derived from the multistage classical flowshop with a single machine per stage, while the third approach uses classical dispatching priority rules. The fourth and fifth approaches, respectively, use random scheduling and local search techniques. Statistical analysis is carried out in order to compare the heuristics and to select the best of them for each shop configuration. Already-existing and new lower bounds on the single stage subproblem are also presented and compared. Three new lower bounds are developed: a dual heuristic based bound, a partially preemptive bound and a heuristic for the so-called subset bound. Some of these lower bounds use a network flow algorithm. A new version of the “Preflow Push” algorithm which runs faster than the original one is presented. The best lower bounds are selected based on numerical tests. Two branch and bound algorithms are presented, an improved version of the sequence enumeration method and a generalization of the so-called interval branching method, along with several bounding strategies. Based on the upper and lower bound studies, several branch and bound algorithms are presented and compared using numerical tests on different shop floor configurations. Eventually, an Object Model for Scheduling Algorithm Implementations (OMSAI), that has been used for the computer implementation of the developed algorithms, is presented.
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Coulthurst, David James. "Ray tracing methods fo hybrid global illumination algorithms." Thesis, University of Bristol, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.555616.
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Global illumination algorithms provide a way to model the different light transport phenomena seen in real life, and produce accurate images. The amount of computation to achieve accurate rendering is large, resulting in the development of many different ways of speeding it up. Some of these focus on speeding up the basic processes of rendering, such as ray tracing operations, and some on leveraging parallel hardware to speed up rendering. Two novel algorithms of this type are described in this thesis. One to allow incoherent paths to be efficiently be traced in parallel by offsetting the latency. The other discovers and exploits empty regions of space to avoid the use of acceleration structures for such operations as soft shadowing and metropolis light transport mutations. The other approach to speeding up rendering is to use a more elegant algorithm. Two such families of algorithms are photon mapping and metropolis light transport. Extensions to progressive photon mapping and hybrid algorithms using photon mapping and metropolis light transport are presented., showing significant speedup in complex scenes that present difficulties to current rendering algorithms.
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Sun, Jianyong. "Hybrid estimation of distribution algorithms for optimization problems." Thesis, University of Essex, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.423548.
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Torres, Reynier Hernández. "Vibration-based damage identification using hybrid optimization algorithms." Instituto Nacional de Pesquisas Espaciais (INPE), 2017. http://urlib.net/sid.inpe.br/mtc-m21b/2017/08.18.16.12.
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The inverse problem of structural damage identification is addressed in this thesis. The inverse solution is obtained by solving an optimization problem using different hybrid algorithms. The forward structural model is solved by Finite Element codes. FORTRAN code developed by the research group of the Laboratório Associado de Computação e Matemática Aplicada (LAC) of the Instituto Nacional de Pesquisas Espaciais (INPE) was applied to some problems, and for other numerical experiments the NASTRAN software was employed. The acceleration, velocity or displacement time history could be used as experimental data in this methodology. The objective function is formulated as the sum of the squared difference between the measured displacement and the data calculated by the forward model. Different hybrid metaheuristics are tested, using a two-step approach. The first step performs the exploration, and the second one carries out the exploitation, starting from the best solution found in the first step. One optimization approach combines the Multi-Particle Collision Algorithm (MPCA) with the Hooke-Jeeves (HJ) direct search method. MPCA is improved using different mechanisms derived from the Opposition-Based Learning, such as Center-Based Sampling and Rotation-Based Learning. Other applied optimizer is the novel q-gradient, and it is also hybridized with HJ method. The methodology is tested on structures with different complexities. Time-invariant damage was assumed to generate the synthetic measurements. Noiseless and noisy data were considered in tests using models implemented in FORTRAN. Most of the experiments were performed using a full set of data, from all possible nodes, and an experiment was done using a reduced dataset with a low level of noise in data. Noiseless data were considered with experiments using NASTRAN. In this case, the experiments were performed using a full set of data. In general, good estimations for damage location and severity are achieved. Some false positives have appeared, but damages were well identified.O problema inverso da identificação de danos estruturais é abordado nesta tese. A solução inversa é obtida resolvendo um problema de otimização usando diferentes algoritmos híbridos. O modelo direto estrutural é resolvido pelo Método dos Elementos Finitos. Código FORTRAN desenvolvido pelo grupo de pesquisa do Laboratório Associado de Computação e Matemática Aplicada (LAC) do Instituto Nacional de Pesquisas Espaciais (INPE) foi aplicado em alguns problemas e, para outros experimentos numéricos, o software NASTRAN foi empregado. O histórico de tempo de aceleração, velocidade ou deslocamento pode ser usado como dados experimentais nesta metodologia. A função objetivo é formulada como a soma da diferença quadrática entre o deslocamento medido e os dados calculados pelo modelo direto. Diferentes metaheurísticas híbridas são testadas, usando uma abordagem em duas etapas. A primeira etapa realiza a exploração em todo o espaço de busca, e a segunda etapa realiza a intensificação a partir da melhor solução encontrada pela primeira etapa. Uma abordagem de otimização combina o Algoritmo de Colisão de Múltiplas Partículas (MPCA) com o método de busca direta Hooke-Jeeves (HJ). O MPCA é melhorado usando diferentes mecanismos derivados da Aprendizagem Baseada na Oposição, como são a Amostragem Baseada no Centro, e a Aprendizagem Baseada em Rotação. Outro otimizador aplicado é o novo q-gradiente, que também é hibridado com o método HJ. A metodologia é testada em estruturas com diferentes complexidades. Supõe-se que os danos são invariante no tempo para gerar as medidas experimentais sintéticas. Dados sem ruído e com diferentes níveis de ruído foram considerados em testes usando modelos implementados em FORTRAN. A maioria dos experimentos foi realizada usando um conjunto completo de dados, de todos os nós possíveis, e um dos experimentos foi feito usando um conjunto incompleto de dados, com um baixo nível de ruído. Para os experimentos utilizando o NASTRAN, foram considerados dados sintéticos sem ruído, e foi utilizado o conjunto completo de dados. Em geral, boas estimativas para localização e gravidade do dano foram alcançadas. Alguns falsos positivos apareceram nas estimativas, mas os danos presentes nos sistemas foram bem identificados.
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Connor, Andrew Miles. "The synthesis of hybrid mechanisms using genetic algorithms." Thesis, Liverpool John Moores University, 1996. http://researchonline.ljmu.ac.uk/5570/.
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This thesis presents a novel design methodology for the synthesis of hybrid mechanisms using Genetic Algorithms. GAs are a search and optimisation method which model the mechanics of population genetics to give a truly global search method. In parallel to the development of a suitable GA, the work also develops novel objective function criteria which go some way to providing an approximation to dynamic criteria whilst using only kinematic properties during calculations. This has considerable effect in reducing the time required to find a feasible solution. The thesis presents a set of results which validate the proposed methodology, both in terms of speed of convergence and quality of the final solutions obtained. The application chosen is the synthesis of a hybrid five bar path generating mechanism. A description is given of the development of a practical machine for a given test case, so as to illustrate that the solutions produced are feasible in terms of real world implementation. Results are presented which show the effectiveness of the machine. Finally, a critical analysis of both the methodology and the results is carried out. This highlights some areas in which the methodology could be improved by future work.
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Krivosheev, Evgeny. "Crowd and Hybrid Algorithms for Cost-Aware Classification." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/263787.
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Classification is a pervasive problem in research that aims at grouping items in categories
according to established criteria. There are two prevalent ways to classify items of interest: i) to
train and exploit machine learning (ML) algorithms or ii) to resort to human classification (via
experts or crowdsourcing).
Machine Learning algorithms have been rapidly improving with an impressive performance in
complex problems such as object recognition and natural language understanding.
However, in many cases they cannot yet deliver the required levels of precision and recall, typically
due to difficulty of the problem and (lack of) availability of sufficiently large and clean datasets.
Research in crowdsourcing has also made impressive progress in the last few years, and the
crowd has been shown to perform well even in difficult tasks [Callaghan et al., 2018; Ranard et al.,
2014]. However, crowdsourcing remains expensive, especially when aiming at high levels of
accuracy, which often implies collecting more votes per item to make classification more robust to
workers' errors.
Recently, we witness rapidly emerging the third direction of hybrid crowd-machine classification
that can achieve superior performance by combining the cost-effectiveness of automatic machine
classifiers with the accuracy of human judgment.
In this thesis, we focus on designing crowdsourcing strategies and hybrid crowd-machine
approaches that optimize the item classification problem in terms of results and budget. We start
by investigating crowd-based classification under the budget constraint with different loss
implications, i.,e., when false positive and false negative errors carry different harm to the task.
Further, we propose and validate a probabilistic crowd classification algorithm that iteratively
estimates the statistical parameters of the task and data to efficiently manage the accuracy vs. cost
trade-off. We then investigate how the crowd and machines can support each other in tackling
classification problems. We present and evaluate a set of hybrid strategies balancing between
investing money in building machines and exploiting them jointly with crowd-based classifiers.
While analyzing our results of crowd and hybrid classification, we found it is relevant to study the
problem of quality of crowd observations and their confusions as well as another promising
direction of linking entities from structured and unstructured sources of data. We propose crowd
and neural network grounded algorithms to cope with these challenges followed by rich evaluation
on synthetic and real-world datasets.
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Assunção, Cláudio Fernando Sequeira. "Hybrid link-state path-vector protocol ++." Master's thesis, Faculdade de Ciências e Tecnologia, 2012. http://hdl.handle.net/10362/7561.
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Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores
Mestrado Integrado em Engenharia Electrotécnica e de ComputadoresO protocolo usado actualmente na Internet para realizar encaminhamento inter-domínio é o BGP (Border Gateway Protocol). Este protocolo foi desenhado para obter acessibilidade dos domínios e não está preparado para todos os requisitos das redes modernas, sofrendo de problemas graves, como convergência lenta e escalabilidade limitada. O protocolo HLP (Hybrid Link-state Path-vector) foi proposto como uma possível solução para estes problemas. Nesta dissertação é avaliado o desempenho do HLP face ao BGP para a Internet real. Também é avaliada a sua compatibilidade com o modelo de negócios da Internet. O estudo do HLP revelou que não é compatível com o actual modelo de negócios da Internet, tendo sido desenvolvido o HLP++, um novo protocolo que corrige esta limitação. A implementação do HLP++ foi realizada no simulador de redes 2 (ns-2). É efectuado um estudo sobre a natureza topológica da Internet, por forma a melhor compreender as relações entre os ASes. Deste estudo resultou uma topologia (um subconjunto da rede Internet) utilizada nas experiências realizadas com os protocolos avaliados, conseguindo assim resultados mais próximos da realidade. Os resultados mostram que o protocolo HLP++ não está adaptado à topologia da Internet, tendo um desempenho inferior quando comparado com o BGP. Apenas tem bom desempenho numa rede hierárquica.
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Ait-Hammouda, Islam. "Modélisation hybride des algorithmes d'anti-blocage des roues (ABS)." Paris 11, 2007. http://www.theses.fr/2007PA112075.
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Dans cette thèse on s'intéresse au problème du contrôle longitudinal des véhicules, plus particulièrement aux algorithmes d'anti-blocage des roues -ABS-. L'objectif est de modéliser et d'étudier une catégorie de systèmes anti-blocage de roues proche de celles utilisées dans l'industrie -basée sur les seuils de décélération-. D'abord, à partir de l'analyse des trajectoires du système, on proposera des conditions sur les seuils de décélération qui optimisent le freinage du véhicule. Ensuite, on s'intéressera à l'impact d'une discontinuité de l'adhérence pneu/sol sur la dynamique du système, un algorithme ABS qui prenne en charge ce type de phénomènes est proposé et étudié. Il nous sera aussi utile pour analyser, sous forme de simulations, l'impact des variations de charge sur les performances des algorithmes ABS. L'interaction des systèmes de sécurité offre de nos jours de nouvelles opportunités pour augmenter performances du véhicule et améliorer le confort du conducteur. Ceci nous a poussé à étudier l'interaction entre les algorithmes ABS des quatre roues. Des solutions à des problèmes tel que : les remontées d'efforts au volant, le manque de conditions satisfaisantes à l'estimation de la vitesse véhicule (garantir au moins une roue dans la zone stable du pneumatique), sont proposées. La dernière partie de cette thèse est consacrée au suivi de trajectoire d'un véhicule avec point de visée. Des lois de commande par retours d'états statiques en l'erreur et de type saturation emboîtée sont proposées. L'objectif de cette partie étant d'introduire l'étude d'un modèle conducteur, qui nous sera utile à l'analyse des performances des algorithmes ABSIn this thesis we were interested on the problem of longitudinal control of vehicles, and more particularly on anti-lock brake algorithms or ABS. The main objective is to model and to study a category of anti-lock brake systems similar to those used in industry (based on deceleration thresholds). Starting from the analysis of the system's trajectories, we will propose conditions on the deceleration thresholds that optimize the braking force of the vehicle. Then, we will be interested in the impact of discontinuous transitions of road characteristics on the system's dynamic. An ABS algorithm that deals with this type of phenomena is proposed and studied. It will be also useful to analyze, using simulations, the impact of vertical force variation on the ABS algorithms performances. Nowadays, The safety systems interaction offers new advisabilities to increase vehicle performances and to improve the driver's comfort. This pushed us to study the interaction between the ABS algorithms of the four wheels. Some solutions that deal with problems such as: the increase of efforts at the wheel, the lack of satisfactory conditions to the estimate the vehicle's speed (to guarantee at least one wheel in the stable zone of the tire), are proposed. The last part of this thesis is devoted to the trajectory following of a vehicle with point of vision. Feedback static controllers (Saturation Type) are proposed. The objective of this part being to introduce the study of a driver model, which will be useful for the analysis of the of the performances of ABS algorithms performances
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Potarusov, Roman. "Разработка и исследование бионических методов упаковкиUne approche hybride parallèle pour le problème du conditionnement unidimensionnel." Artois, 2008. http://www.theses.fr/2008ARTO0205.
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Le problème du bin packing (BPP) est l’un des problèmes les plus connus dans le domaine de la recherche opérationnelle et de l’optimisation combinatoire. Sa version classique est le bin packing à une seule dimension mais il existe de nombreuses variantes en deux ou trois dimensions. Le BPP à une dimension fait l’objet de plusieurs applications industrielles à savoir la découpe de câbles ; le remplissage de camions ou de containers avec comme seule contrainte le poids ou le volume des articles. Ce problème fait partie de la classe des problèmes NP-difficiles. C’est pourquoi des heuristiques et des méta-heuristiques sont utilisées pour trouver des solutions approchées mais de bonne qualité. Dans cette thèse nous nous intéressons au BPP à une dimension. Nous développons un algorithme génétique parallèle hybride pour le résoudre. Deux modes d’évolution ont été adaptés et intégrés dans cet algorithme : le mode de Vries et le mode de Lamarck. De nouveau opérateurs génétiques ont été utilisés. Deux méthodes de recherche locale ont été utilisées pour améliorer la qualité des solutions existantes dans la littérature. Notre étude expérimentale montre que notre algorithme proposé donne des solutions quasi-optimales ou optimales pour toutes les instances connues dans la littérature dans un temps de calcul raisonnable. Dans le cas de solutions quasi-optimales la déviation de la solution optimale ne dépasse jamais un seul « bin ». Nos futures recherches vont porter sur l’application de l’algorithme proposé (pour résoudre le BPP) à la résolution du problème de tournée de véhicules avec multiple routesOne-dimensional Bin Packing Problem (BPP) is well known combinatorial optimization problem. BPP in its general form is P-hard in the strong sense, so there is a little hope of finding even pseudo-polynomial time optimization algorithm for it. BPP is an interesting topic of research, because BPP is encountered in many industries, such as steel, glass and paper manufacturing. There are many other industrial problems that seem to be different, but have a very similar structure, such as capital budgeting, processor scheduling and VLSI design. BPP models several practical problems in computer science. Some examples are: table formatting, prepaging, file allocation. In this thesis the Hybrid Parallel Genetic Algorithm to solve 1-D BPP has been presented. Two evolution models (de Vries’ evolution model and Lamarck’s evolution model) have been adapted to solve the BPP. New problem-oriented genetic operators have also been developed. They never decrease the quality of solution and allow obtaining valid BPP solutions. Two effective local search algorithms are proposed. They allow improving of BPP solutions to get quasi-optimal and optimal packings. Computational experiments show that the presented algorithm gives quasi-optimal and optimal solutions for all benchmark instances in an acceptable amount of computing time, clearly showing the robustness of the proposed approach. In the case of quasi-optimal solutions the absolute deviation from reference solution is at most one bin. Future work could explore the possibility of designing more sophisticated architectures of genetic search with migration and applying the proposed approach to solve the Vehicle Routing Problem with Multiple Routes. BPP approach seems to be effective to distribute routes to vehicles
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Sbihi, Abdelkader. "Les Méthodes Hybrides en Optimisation Combinatoire :Algorithmes Exacts et Heuristiques." Phd thesis, Université Panthéon-Sorbonne - Paris I, 2003. http://tel.archives-ouvertes.fr/tel-00012188.
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La thèse se situe dans le domaine de l'optimisation combinatoire, en particulier celui de lamodélisation et de la résolution algorithmique. Dans cette thèse, nous étudions deux variantes
NP-difficiles de problèmes de type sac-à-dos. Plus précisément, nous traitons le problème de
la distribution équitable (le Knapsack Sharing Problem : KSP) et le problème du sac-à-dos
généralisé à choix multiple (le Multiple-choice Multidimensional Knapasck Problem : MMKP).
Dans la première partie de cette thèse, nous nous intéressons au développement d'algorithmes
approchés pour les deux variantes évoquées du problème de type sac-à-dos. La deuxième partie
traite essentiellement de la résolution exacte du problème du sac-à-dos généralisé à choix multiple.
L'approche exacte que nous proposons est de type séparation et évaluation s'appuyant
principalement sur : (i) le calcul des bornes inférieure et supérieure et (ii) l'utilisation de la
stratégie par le meilleur d'abord en développant des branches à double noeuds fils et frère.
La première partie porte sur l'étude et la résolution approchée des deux problèmes KSP et
MMKP. Concernant le problème de la distribution équitable, nous proposons dans un premier
temps, une première version de l'algorithme exploitant certaines caractéristiques de la
recherche tabou. Dans un deuxième temps, nous développons une deuxième version de l'algorithme dont l'idée principale consiste à tenter de combiner l'intensification de la recherche dans l'espace des solutions et la diversification de la solution obtenue. Nous soulignons la rapidité
de la première version et l'efficacité de la deuxième. Ensuite nous nous intéressons au problème
de sac-à-dos généralisé à choix multiple. Nous proposons deux heuristiques de recherche locale
itérative. Le premier algorithme s'appuie sur une “recherche guidée”. Le deuxième algorithme
est une recherche locale que nous appelons réactive avec stratégies de déblocage et de dégradtion améliorantes de la solution et basées sur l'inter-change local.
Dans la deuxième partie de cette thèse, nous proposons une méthode de résolution exacte de type séparation et évaluation pour le problème du sac-à-dos généralisé à choix multiple. D'une part, nous nous proposons la réduction du problème initial au problème auxiliaire MMKPaux qui n'est autre que le problème de sac-à-dos à choix multiple MCKP. Nous calculons une borne supérieure pour le MMKPaux et nous établissons le résultat théorique pour lequel une borne supérieure pour le MMKPaux est une borne supérieure pour le MMKP. D'autre part, nous proposons le calcul d'une borne supérieure ainsi qu'une borne inférieure de départ pour le problème étudié qui sont nécessaires pour la réduction de l'espace de recherche. L'étude expérimentale montre l'efficacité de la méthode proposée sur différents groupes d'instances de petite et moyenne taille.
Nous expliquons enfin pourquoi cet algorithme exact atteint ses limites de résolution, dˆues
principalement à la complexité intrinsèque du modèle étudié. D'autant la résolution dépend de
la taille et la densité des instances traitées.
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Rondepierre, Aude. "Algorithmes hybrides pour le contrôle optimal des systèmes non linéaires." Phd thesis, Grenoble INPG, 2006. http://tel.archives-ouvertes.fr/tel-00112203.
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Cette thèse est consacrée à la résolution des problèmes de contrôle non linéaires par des méthodes de calcul hybride. L'idée défendue est que la modélisation par les systèmes hybrides permet la résolution approchée des problèmes non linéaires sans connaissance a priori du comportement du système étudié. Dans un premier temps, nous nous intéressons à la modélisation des systèmes de contrôle non linéaires par une nouvelle classe de systèmes hybrides affines par morceaux. Un soin particulier est apporté à l'étude de l'erreur et de la convergence de l'approximation hybride. La deuxième partie est consacrée au problème de la contrôlabilité à l'origine des systèmes non linéaires. Nous nous intéressons tout d'abord à la quantification de l'erreur commise entre le domaine contrôlable non linéaire et son approximation hybride. Nous proposons ensuite une approche constructive pour le calcul du domaine contrôlable, permettant alors de réduire l'exploration des états discrets de l'automate hybride. La dernière partie est dédiée à la recherche de solutions optimales des problèmes de contrôle non linéaires et hybrides. Nous justifions tout d'abord la pertinence de la modélisation hybride à travers deux approches : le principe du maximum de Pontryagin et les solutions de viscosité des équations d'Hamilton-Jacobi-Bellman. Nous énonçons en particulier un principe du maximum hybride qui nous permet alors de déterminer la structure du contrôle optimal hybride. Ces trois parties répondent à un objectif principal : développer par le calcul hybride combinant analyse numérique et calcul formel, des outils mathématiques et algorithmiques efficaces pour l'étude de dynamiques contrôlées non linéaires.
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Andersson, Martin. "Industrial scheduling with evolutionary algorithms using a hybrid representation." Thesis, Högskolan i Skövde, Institutionen för teknik och samhälle, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-5348.
Full textAbstract:
Scheduling problems have been studied extensively in the literature but because they are so hard to solve, especially real-world problems, it is still interesting to find ways of solving them more efficiently. This thesis aims to efficiently solve a real-world scheduling problem by using a hybrid representation together with an optimisation algorithm. The aim of the hybrid representation is to allow the optimisation to focus on the parts of the scheduling problem where it can make the most improvement. The new approach used in this thesis to accomplish this goal, is the combination of simulation-based optimisation using genetic algorithms and dispatching rules. By using this approach, it is possible to investigate the effect of putting specified job sequences in certain machines and using dispatching rules in the other. The hypothesis is that the optimisation can use dispatching rules on non-bottleneck machines that have little impact on the overall performance of the line and some specified job sequences on bottleneck machines that are hard to be scheduled efficiently with dispatching rules. This would allow the optimisation to focus on the bottleneck machines and that would produce a more efficient search. The results from the case study shows it is a viable approach exceeding or equalling existing techniques. The hypothesis that the optimisation can focus its efforts is supported by a bottleneck analysis which corresponds with the experimental results from optimisations.
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Camacho, Navarro Jhonatan. "Robust structural damage detection by using statistical hybrid algorithms." Doctoral thesis, Universitat Politècnica de Catalunya, 2019. http://hdl.handle.net/10803/667239.
Full textAbstract:
This thesis presents the results of applying a statistical hybrid approach for structural health monitoring using piezo actuating signals. Where, by combining statistical processing based on Principal Component Analysis (PCA), cross-correlation functions and pattern recognition methods it was possible to detect, classify and locate damages under varying environmental conditions and possible sensor faults. The proposed methodology consists of first transmiting/sensing guided waves along the monitored structure surface by using piezoelectric (PZT) devices. Then, cross-correlated piezoelectric signals are statistically represented by means of a PCA model. Later, damages are identified through error indexes computed from a statistical baseline model. Finally, clustering methods and scattered plots are used to verify the performance of the proposed algorithm. Improved or new techniques are presented in this thesis which were focused to achieve more reliable diagnosis with high robustness and good performance. Specifically, differential genetic algorithms are used for automatically tuning parameters in a PCA-SOM damage detection/classification approach. Additionally, Ensemble Learning is explored as approach for obtaining more efficient diagnosis with high separable boundaries between undamaged and damage conditions taking advantages of learner algorithms built from Non-Linear PCA and a Multiactuacting active scheme of piezodiagnostics. Also, a modified version of the Reconstruction Algorithm for Probabilistic Inspection of Damage – RAPID is implemented to solve location tasks in SHM. The proposed methodology was experimentally evaluated on different structures such a a carbon-steel pipe loop, a laminate plate, aircraft wings and a scale tower wind, among others; where different damage scenarios were studied, including leaks scenarios, mass adding and cuts. The effectiveness of the proposed methodology to detect, locate and classify damages under varying environmental and operational conditions is demonstrated. Likewise, the feasibility for continuous monitoring is validated by embedding the code of the proposed algorithm whose capacity to detect structural damages was demonstrated. As a result, the combination of piezodiagnostics approach, cross-correlation analysis, principal component analysis, clustering techniques and Ensemble Learning become as promising solution in the field of structural health monitoring and specifically to achieve a robust solution for damage detection and location.Esta tesis presenta los resultados de la aplicación de un enfoque híbrido estadístico para el monitoreo de salud estructural utilizando señales piezoeléctrica. Donde, al combinar procesamiento estadístico basado en análisis de componentes principales (PCA), funciones de correlación cruzada y métodos de reconocimiento de patrones fue posible detectar, clasificar y localizar daños en diferentes condiciones ambientales y posibles fallas en los sensores. La metodología desarrollada consiste en primero transmitir ondas guiadas a lo largo de la superficie de la estructura monitorizada mediante el uso de dispositivos piezoeléctricos (PZT). Luego, las señales de correlación cruzada calculadas sobre las mediciones piezoeléctricas se representan estadísticamente por medio de un modelo de línea base obtenido mediante PCA. Posteriormente, los daños se identifican mediante índices de error calculados a partir del modelo estadístico de referencia. Finalmente, se utilizan métodos de aprendizaje no supervisado y gráficos de dispersión para verificar el rendimiento del algoritmo propuesto. En esta tesis se presentan nuevas técnicas o versiones mejoradas para lograr un diagnóstico más confiable con alta robustez y buen rendimiento. Específicamente, se utilizan algoritmos genéticos diferenciales para ajustar automáticamente los parámetros en un algoritmo de clasificación y detección de daños basado en PCA y Mapas auto-organizados (SOM). Además, se analiza Ensemble Learning como un enfoque para obtener un diagnóstico más eficiente con mejores fronteras de separación entre condiciones con y sin daño, combinando diferentes algoritmos de aprendizaje construidos a partir de PCA no lineal y lineal así como un esquema activo de multiactuación de piezodiagnóstico. Adicionalmente, se implementa una versión modificada del algoritmo de reconstrucción para la inspección probabilística de daños (RAPID) para estimar la localización del daño. La metodología propuesta se validó experimentalmente en diferentes estructuras, como un circuito de tubería de acero al carbono, una placa laminada, alas de avión y un generador de viento a escala, entre otros; donde se estudiaron diferentes escenarios de daños, incluidos escenarios de fugas, agregación de masa y grietas. Se demuestra la efectividad de la metodología propuesta para detectar, localizar y clasificar daños en diferentes condiciones ambientales y operativas. Del mismo modo, la viabilidad del monitoreo continuo se valida implementando el código del algoritmo propuesto en un sistema embebido, cuya capacidad para detectar daños estructurales se demostró. Como resultado, la combinación del enfoque de piezodiagnóstico, análisis de correlación cruzada, análisis de componentes principales, técnicas de aprendizaje no supervisado y Ensemble Learning se obtiene una solución prometedora en el campo del monitoreo de la salud estructural y específicamente para lograr una solución robusta para la detección de daños y la ubicación.
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Ryan, Mark Desmond Charles. "Hybrid genetic algorithms for real world combinatorial optimization problems." Thesis, University of East Anglia, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.393303.
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Jin, Yan. "Hybrid metaheuristic algorithms for sum coloring and bandwidth coloring." Thesis, Angers, 2015. http://www.theses.fr/2015ANGE0062/document.
Full textAbstract:
Le problème de somme coloration minimum (MSCP) et le problème de coloration de bande passante (BCP) sont deux généralisations importantes du problème de coloration des sommets classique avec de nombreuses applications dans divers domaines, y compris la conception de circuits imprimés, la planication, l’allocation de ressource, l’affectation de fréquence dans les réseaux mobiles, etc. Les problèmes MSCP et BCP étant NP-difficiles, les heuristiques et métaheuristiques sont souvent utilisées en pratique pour obtenir des solutions de bonne qualité en un temps de calcul acceptable. Cette thèse est consacrée à des métaheuristiques hybrides pour la résolution efcace des problèmes MSCP et BCP. Pour le problème MSCP, nous présentons deux algorithmes mémétiques qui combinent l’évolution d’une population d’individus avec de la recherche locale. Pour le problème BCP, nous proposons un algorithme hybride à base d’apprentissage faisant coopérer une méthode de construction “informée” avec une procédure de recherche locale. Les algorithmes développés sont évalués sur des instances biens connues et se révèlent très compétitifs par rapport à l’état de l’art. Les principaux composants des algorithmes que nous proposons sont également analysésThe minimum sum coloring problem (MSCP) and the bandwidth coloring problem (BCP) are two important generalizations of the classical vertex coloring problem with numerous applications in diverse domains, including VLSI design, scheduling, resource allocation and frequency assignment in mobile networks, etc. Since the MSCP and BCP are NP-hard problems, heuristics and metaheuristics are practical solution methods to obtain high quality solutions in an acceptable computing time. This thesis is dedicated to developing effective hybrid metaheuristic algorithms for the MSCP and BCP. For the MSCP, we present two memetic algorithms which combine population-based evolutionary search and local search. An effective algorithm for maximum independent set is devised for generating initial solutions. For the BCP, we propose a learning-based hybrid search algorithm which follows a cooperative framework between an informed construction procedure and a local search heuristic. The proposed algorithms are evaluated on well-known benchmark instances and show highly competitive performances compared to the current state-of-the-art algorithms from the literature. Furthermore, the key issues of these algorithms are investigated and analyzed
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Knowles, Joshua D. "Local-search and hybrid evolutionary algorithms for Pareto optimization." Thesis, University of Reading, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.394429.
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Alluhaibi, Osama. "Hybrid precoding algorithms for millimeter-wave massive MIMO systems." Thesis, University of Kent, 2018. https://kar.kent.ac.uk/67007/.
Full textAbstract:
The large available spectrum efficiency and wider bandwidth at millimeter wave (mm-Wave) frequencies can enable the gigabit-per-second data rates needed for next generation wireless systems. To compensate for the high propagation loss at mm-Wave bands, multiple-input multiple-output (MIMO) with a large number of antennas are usually employed to enable beamforming. Therefore, a combination of the large number of antennas which can be called massive MIMO technology with mm-Wave bands are considered as one solution for substantially increasing the data rate for future wireless communication systems. The theoretical benefits of large antenna array systems are based on the fact that the number of radio frequency (RF) chains is equivalent to the number of antennas in the conventional wireless communication system which is known as the fully digital system. Nevertheless, implementing a large number of RF chains can be problematic since it increases the system cost, power consumption, high complexity and lowers power efficiency The overall objective of this thesis is to provide simple and effective hybrid D/A precoding and combing for mm-Wave large antenna array systems. Firstly, hybrid D/A precoding with a small number of RF chains is being considered for mm-Wave large antenna array systems. Currently, two types of antenna structures, fully-connected antenna array and partially-connected antenna array structures are adopted in the literature. Considering that each antenna array structure has its own practical advantage, in this thesis, by addressing both structures hybrid D/A precoding algorithms are proposed with target of maximizing the system's spectral efficiency with low computational complexity. For a fully-connected antenna array structure, the precoding design is formulated as an optimization problem to minimize the Euclidean distance between the hybrid D/A and the fully digital system. For a partially-connected antenna array structures, the hybrid D/A precoding is formulated as a joint D/A optimization to maximize the spectral efficiency of the system. This work further develops hybrid D/A precoding designs for mm-Wave multi-user systems based on maximizing the sum rate of the system directly. It will be shown that the proposed algorithms outperform the existing hybrid D/A precoding algorithms for the two types of structures, in terms of the spectral efficiency. Secondly, energy efficient and low complexity hybrid D/A system for mm-Wave large antenna array systems is proposed to reduce the power consumption at the system. The energy efficiency criteria is formulated as fractional programming maximization problem. The target is to find the optimal number of RF chains as the RF chains consume a high energy at the system. Therefore, the effective optimal number of RF chains of the system is found by proposing a simple search algorithm. Then, two methods are proposed for designing low complexity analog and digital precoders and combiners. The presented solutions for the hybrid D/A system are shown to be effective, as these approaches can achieve high energy efficiency, and low computational complexity as compared to the existing algorithms in hybrid D/A paradigms. Ultimately, the proposed D/A precoders and combiners based on the fully-connected antenna array attain an asymptotically optimal achievable spectral efficiency to that of the fully digital system. Thirdly, uplink multi-user hybrid D/A precoding and combining design for mm-Wave large antenna array systems is investigated. The intrinsic focus of this work is to reduce the interference of the system in the analog and digital precoders and combiners. Considering the possibility that uplink transmissions from different users can go through the paths sharing the same physical scatters, some transmission paths of different users may have overlapped angle of arrivals (AoAs) at the base station. Under this circumstance, the correlation between the channel vectors also increases highly, which affects the achievable uplink rate severely. The underlying concentrate of this work is to reduce the interference caused by users sharing the same scatterers during simultaneous uplink transmission. Therefore, in this thesis, by taking account the channel correlation between users sharing the overlapped AoAs, the genuine focus is on substantially maximizing the desired signal of a piratical user while reducing the system interference. Furthermore, the channel estimation is investigated by designing a two-step procedure. The strongest power point in each scattering point is detected and the accuracy is improved by employing an angler domain scheme. Extensive simulations demonstrate that the achievable uplink rate of our proposed algorithms surpasses the achievable uplink rate of the existing algorithms in hybrid D/A paradigms in the practical scenarios.
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Wakelam, Mark. "Intelligent hybrid approach for integrated design." Thesis, University of Nottingham, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.263942.
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Silva, Maurício Rodrigues. "Um novo método híbrido aplicado à solução de sistemas não-lineares com raízes múltiplas." Universidade do Estado do Rio de Janeiro, 2009. http://www.bdtd.uerj.br/tde_busca/arquivo.php?codArquivo=1559.
Full textAbstract:
Este trabalho tem como objetivo apresentar soluções de sistemas não-lineares com raízes múltiplas, através de um algoritmo híbrido. Para esta finalidade foi desenvolvido
e implementado um algoritmo de busca aleatória baseado no método proposto por Luus e Jaakola (1973) como etapa de busca aleatória dos pontos iniciais, que são refinados
através do algoritmo de Hooke e Jeeves. O diferencial deste trabalho foi propor um algoritmo híbrido, utilizando as características dos algoritmos Luus-Jaakola e Hooke e
Jeeves como etapas de busca e refinamento respectivamente. Para isso, os algoritmos acima são encapsulados em funções no algoritmo híbrido. Além destas duas etapas, o algoritmo híbrido possui duas outras características importantes, que é a execução repetida até que se alcance um número suficiente de soluções distintas, que são então submetidas a um processo de classificação de soluções por intervalo, onde cada intervalo gera um conjunto de soluções próximas, que por sua vez, são submetidas à etapa final de minimização, resultando em apenas um valor de solução por classe. Desta forma cada classe produz uma única solução, que faz parte do conjunto final de soluções do problema, pois este algoritmo é aplicado a problemas com múltiplas soluções. Então, o algoritmo híbrido desenvolvido foi testado, tendo como padrão, vários problemas clássicos de programação não-linear, em especial os problemas irrestritos com múltiplas soluções. Após os testes, os resultados foram comparados com o algoritmo Luus-Jaakola, e o Método de Newton Intervalar / Bisseção Generalizada (IN/GB - Interval Newton/Generalized Bisection), com a finalidade de se obter uma
análise quantitativa e qualitativa de seu desempenho. Por fim comprovou-se que o algortimo Híbrido obteve resultados superiores quando comparados com os demais.This paper aims to present solutions for nonlinear systems with multiple roots, using a hybrid algorithm. For this purpose was developed and implemented an algorithm based on random search method proposed by Luus and Jaakola (1973) as a step in search of random starting points, which will be refined through the algorithm of Hooke and Jeeves. The differential of this work is to propose a hybrid algorithm, using the characteristics of the Luus-Jaakola algorithm and Hooke and Jeeves as a search and refinement stages respectively. For this, the above algorithms are encapsulated in functions in the hybrid algorithm. Besides these two steps, the hybrid algorithm has two other important characteristics, which is the execution repeated until to reach a sufficient number of distinct solutions, which is then undergo a process of classification of solutions by interval, where each interval generates a set solutions to close, which in turn is subject to the final stage of minimization, resulting in only one value per class of solution. Thus each class provides a unique solution, which is part of the final set of solutions of the problem, because this algorithm is applied to problems with multiple solutions. So, the hybrid algorithm developed was tested, with the standard, several problems of classical non-linear programming, in particular the unrestricted problems with multiple solutions. After the tests, the results were compared with algorithm Luus-Jaakola, and the Interval Newton/Generalized Bisection method (IN/GB), in order to obtain a quantitative and qualitative analysis of their performance. Finally it was found that the hybrid algortimo achieved higher when compared to the others.