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Uskay, Selim Onur. "Route Optimization For Solid Waste Transportation Using Parallel Hybrid Genetic Algorithms." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612942/index.pdf.
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The transportation phase of solid waste management is highly critical as it may constitute approximately 60 to 75 percent of the total cost. Therefore, even a small amount of improvement in the collection operation can result in a significant saving in the overall cost. Despite the fact that there exist a considerable amount of studies on Vehicle Routing Problem (VRP), a vast majority of the existing studies are not integrated with GIS and hence they do not consider the path constraints of real road networks for waste collection such as one-way roads and U-Turns. This study involves the development of computer software that optimizes the waste collection routes for solid waste transportation considering the path constraints and road gradients. In this study, two different routing models are proposed. The aim of the first model is to minimize the total distance travelled whereas that of the second model is to minimize the total fuel consumption that depends on the loading conditions of the truck and the road gradient. A comparison is made between these two approaches. It is expected that the two approaches generate routes having different characteristics. The obtained results are satisfactory. The distance optimization model generates routes that are shorter in length whereas the fuel consumption optimization model generates routes that are slightly higher in length but provides waste collection on steeply inclined roads with lower truck load. The resultant routes are demonstrated on a 3D terrain view.
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Wong, Yin-cheung Eugene, and 黃彥璋. "A hybrid evolutionary algorithm for optimization of maritime logisticsoperations." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B44526763.
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Inclan, Eric. "The Development of a Hybrid Optimization Algorithm for the Evaluation and Optimization of the Asynchronous Pulse Unit." FIU Digital Commons, 2014. http://digitalcommons.fiu.edu/etd/1582.
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The effectiveness of an optimization algorithm can be reduced to its ability to navigate an objective function’s topology. Hybrid optimization algorithms combine various optimization algorithms using a single meta-heuristic so that the hybrid algorithm is more robust, computationally efficient, and/or accurate than the individual algorithms it is made of. This thesis proposes a novel meta-heuristic that uses search vectors to select the constituent algorithm that is appropriate for a given objective function. The hybrid is shown to perform competitively against several existing hybrid and non-hybrid optimization algorithms over a set of three hundred test cases. This thesis also proposes a general framework for evaluating the effectiveness of hybrid optimization algorithms. Finally, this thesis presents an improved Method of Characteristics Code with novel boundary conditions, which better characterizes pipelines than previous codes. This code is coupled with the hybrid optimization algorithm in order to optimize the operation of real-world piston pumps.
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Jaber, Ahmed. "Hybrid Algorithm for Multi-objective Mixed-integer Non-convex Mechanical Design Optimization Problems." Thesis, Troyes, 2021. http://www.theses.fr/2021TROY0034.
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Les problèmes d'optimisation sous contraintes non linéaires non convexes multi-objectifs en variables mixtes (discrètes et continues) apparaissent dans de nombreux domaines de l’ingénierie et notamment dans les applications de conception en mécaniques. Cette thèse vise à développer une nouvelle méthode pour résoudre ces problèmes d’optimisation. Notre proposition est une hybridation de l'algorithme multicritère « Branch-and-Bound » (MCBB) avec l’algorithme évolutionnaire de type NSGAII. L'approche proposée est en outre renforcée par de nouvelles stratégies de branchement conçues pour l’algorithme MCBB. Les contraintes du problème d’optimisation sont gérées à l'aide d'une nouvelle technique dédiée aux algorithmes évolutionnaires. Les performances de cette nouvelle approche sont évaluées et comparées à l’existant par une étude statistique sur un ensemble de problèmes tests. Les résultats montrent que les performances de notre algorithme sont compétitives face à l’algorithme NSGAII seul. Nous proposons deux applications de notre algorithme : les applications "Recherche de solutions faisables" et "Recherche de solutions optimales". Celles-ci sont appliquées sur un problème industriel réel d’un réducteur à engrenages à 3 étages formulé comme un problème bi-objectif. Dans ce problème des contraintes sont incluses pour satisfaire aux exigences de normes ISO sur le calcul de la capacité de charge des engrenages<br>Multi-objective mixed-integer non-convex non-linear constrained optimization problems that appears in several fields especially in mechanical applications. This thesis aims to develop a new method to solve such problems. Our proposal is a hybridization of the Multi-Criteria Branch-and-Bound (MCBB) algorithm with the Non-dominated Sorting Genetic Algorithm 2 (NSGAII). The proposed approach is furthermore enhanced by new branching strategies designed for MCBB. The constraints are handled using a new proposed constraint handling technique for evolutionary algorithms. Numerical experiments based on statistical assessment are done in this thesis to examine the performance of the new proposed approach. Results show the competitive performance of our algorithm among NSGAII. We propose two applications of our proposed approach: "Search Feasibility" and "Seek Optimality" applications. Both are applied on a real-world state of art 3 stages reducer problem which is formulated in this thesis to a bi-objective problem to meet the requirement of ISO standards on calculation of load capacity of gears
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Vytla, Veera Venkata Sunil Kumar. "Multidisciplinary Optimization Framework for High Speed Train using Robust Hybrid GA-PSO Algorithm." Wright State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=wright1310558511.
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Meyer, Danielle L. "Energy Optimization of a Hybrid Unmanned Aerial Vehicle (UAV)." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1523493111005807.
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Franz, Wayne. "Multi-population PSO-GA hybrid techniques: integration, topologies, and parallel composition." Springer, 2013. http://hdl.handle.net/1993/23842.
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Recent work in metaheuristic algorithms has shown that solution quality may be improved by composing algorithms with orthogonal characteristics. In this thesis, I study multi-population particle swarm optimization (MPSO) and genetic algorithm (GA) hybrid strategies. I begin by investigating the behaviour of MPSO with crossover, mutation, swapping, and all three, and show that the latter is able to solve the most difficult benchmark functions. Because GAs converge slowly and MPSO provides a large degree of parallelism, I also develop several parallel hybrid algorithms. A composite approach executes PSO and GAs simultaneously in different swarms, and shows advantages when arranged in a star topology, particularly with a central GA. A static scheme executes in series, with a GA performing the exploration followed by MPSO for exploitation. Finally, the last approach dynamically alternates between algorithms. Hybrid algorithms are well-suited for parallelization, but exhibit tradeoffs between performance and solution quality.
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Chhabra, Rupanshi. "Control Power Optimization using Artificial Intelligence for Hybrid Wing Body Aircraft." Thesis, Virginia Tech, 2015. http://hdl.handle.net/10919/56580.
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Traditional methods of control allocation optimization have shown difficulties in exploiting the full potential of controlling a large array of control surfaces. This research investigates the potential of employing artificial intelligence methods like neurocomputing to the control allocation optimization problem of Hybrid Wing Body (HWB) aircraft concepts for minimizing control power, hinge moments, and actuator forces, while keeping the system weights within acceptable limits. The main objective is to develop a proof-of-concept process suitable to demonstrate the potential of using neurocomputing for optimizing actuation power for aircraft featuring multiple independently actuated control surfaces and wing flexibility. An aeroelastic Open Rotor Engine Integration and Optimization (OREIO) model was used to generate a database of hinge moment and actuation power characteristics for an array of control surface deflections. Artificial neural network incorporating a genetic algorithm then performs control allocation optimization for an example aircraft. The results showed that for the half-span model, the optimization results (for the sum of the required hinge moment) are improved by more than 11%, whereas for the full-span model, the same approach improved the result by nearly 14% over the best MSC Nastran solution by using the neural network optimization process. The results were improved by 23% and 27% over the case where only the elevator is used for both half-span and full-span models, respectively. The methods developed and applied here can be used for a wide variety of aircraft configurations.<br>Master of Science
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Amouzgar, Kaveh. "Multi-objective optimization using Genetic Algorithms." Thesis, Högskolan i Jönköping, Tekniska Högskolan, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-19851.
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In this thesis, the basic principles and concepts of single and multi-objective Genetic Algorithms (GA) are reviewed. Two algorithms, one for single objective and the other for multi-objective problems, which are believed to be more efficient are described in details. The algorithms are coded with MATLAB and applied on several test functions. The results are compared with the existing solutions in literatures and shows promising results. Obtained pareto-fronts are exactly similar to the true pareto-fronts with a good spread of solution throughout the optimal region. Constraint handling techniques are studied and applied in the two algorithms. Constrained benchmarks are optimized and the outcomes show the ability of algorithm in maintaining solutions in the entire pareto-optimal region. In the end, a hybrid method based on the combination of the two algorithms is introduced and the performance is discussed. It is concluded that no significant strength is observed within the approach and more research is required on this topic. For further investigation on the performance of the proposed techniques, implementation on real-world engineering applications are recommended.
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Cai, Xinye. "A multi-objective GP-PSO hybrid algorithm for gene regulatory network modeling." Diss., Manhattan, Kan. : Kansas State University, 2009. http://hdl.handle.net/2097/1492.
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Ghoman, Satyajit Sudhir. "A Hybrid Optimization Framework with POD-based Order Reduction and Design-Space Evolution Scheme." Diss., Virginia Tech, 2013. http://hdl.handle.net/10919/23113.
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The main objective of this research is to develop an innovative multi-fidelity multi-disciplinary design, analysis and optimization suite that integrates certain solution generation codes and newly developed innovative tools to improve the overall optimization process. The research performed herein is divided into two parts: (1) the development of an MDAO framework by integration of variable fidelity physics-based computational codes, and (2) enhancements to such a framework by incorporating innovative features extending its robustness.<br /><br />The first part of this dissertation describes the development of a conceptual Multi-Fidelity Multi-Strategy and Multi-Disciplinary Design Optimization Environment (M3 DOE), in context of aircraft wing optimization. M3 DOE provides the user a capability to optimize configurations with a choice of (i) the level of fidelity desired, (ii) the use of a single-step or multi-step optimization strategy, and (iii) combination of a series of structural and aerodynamic analyses. The modularity of M3 DOE allows it to be a part of other inclusive optimization frameworks. The M3 DOE is demonstrated within the context of shape and sizing optimization of the wing of a Generic Business Jet aircraft. Two different optimization objectives, viz. dry weight minimization, and cruise range maximization are studied by conducting one low-fidelity and two high-fidelity optimization runs to demonstrate the application scope of M3 DOE.<br /><br />The second part of this dissertation describes the development of an innovative hybrid optimization framework that extends the robustness of M3 DOE by employing a proper orthogonal decomposition-based design-space order reduction scheme combined with the evolutionary algorithm technique. The POD method of extracting dominant modes from an ensemble of candidate configurations is used for the design-space order reduction. The snapshot of candidate population is updated iteratively using evolutionary algorithm technique of fitness-driven retention. This strategy capitalizes on the advantages of evolutionary algorithm as well as POD-based reduced order modeling, while overcoming the shortcomings inherent with these techniques. When linked with M3 DOE, this strategy offers a computationally efficient methodology for problems with high level of complexity and a challenging design-space. This newly developed framework is demonstrated for its robustness on a non-conventional supersonic tailless air vehicle wing shape optimization problem.<br>Ph. D.
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SINGH, BHUPINDER. "A HYBRID MSVM COVID-19 IMAGE CLASSIFICATION ENHANCED USING PARTICLE SWARM OPTIMIZATION." Thesis, DELHI TECHNOLOGICAL UNIVERSITY, 2021. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18864.
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COVID-19 (novel coronavirus disease) is a serious illness that has killed millions of
civilians and affected millions around the world. Mostly as result, numerous
technologies that enable both the rapid and accurate identification of COVID-19
illnesses will provide much assistance to healthcare practitioners. A machine learning-
based approach is used for the detection of COVID-19. In general, artificial intelligence
(AI) approaches have yielded positive outcomes in healthcare visual processing and
analysis. CXR is the digital image processing method that plays a vital role in the
analysis of Covid-19 disease. Due to the maximum accessibility of huge scale annotated
image databases, excessive success has been done using multiclass support vector
machines for image classification. Image classification is the main challenge to detect
medical diagnosis. The existing work used CNN with a transfer learning mechanism
that can give a solution by transferring information from GENETIC object recognition
tasks. The DeTrac method has been used to detect the disease in CXR images. DeTrac
method accuracy achieved 93.1~ 97 percent. In this proposed work, the hybridization
PSO+MSVM method has worked with irregularities in the CXR images database by
studying its group distances using a group or class mechanism. At the initial phase of
the process, a median filter is used for the noise reduction from the image. Edge
detection is an essential step in the process of COVID-19 detection. The canny edge
detector is implemented for the detection of edges in the chest x-ray images. The PCA
(Principal Component Analysis) method is implemented for the feature extraction
phase. There are multiple features extracted through PCA and the essential features are
optimized by an optimization technique known as swarm optimization is used for
feature optimization. For the detection of COVID-19 through CXR images, a hybrid
multi-class support vector machine technique is implemented. The PSO (particle swarm
optimization) technique is used for feature optimization. The comparative analysis of
various existing techniques is also depicted in this work. The proposed system has
achieved an accuracy of 97.51 percent, SP of 97.49 percent, and 98.0 percent of SN.
The proposed system is compared with existing systems and achieved better
performance and the compared systems are DeTrac, GoogleNet, and SqueezeNet.
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Martins, Marcella Scoczynski Ribeiro. "A hybrid multi-objective bayesian estimation of distribution algorithm." Universidade Tecnológica Federal do Paraná, 2017. http://repositorio.utfpr.edu.br/jspui/handle/1/2806.
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Atualmente, diversas metaheurísticas têm sido desenvolvidas para tratarem problemas de otimização multiobjetivo. Os Algoritmos de Estimação de Distribuição são uma classe específica de metaheurísticas que exploram o espaço de variáveis de decisão para construir modelos de distribuição de probabilidade a partir das soluções promissoras. O modelo probabilístico destes algoritmos captura estatísticas das variáveis de decisão e suas interdependências com o problema de otimização. Além do modelo probabilístico, a incorporação de métodos de busca local em Algoritmos Evolutivos Multiobjetivo pode melhorar consideravelmente os resultados. Estas duas técnicas têm sido aplicadas em conjunto na resolução de problemas de otimização multiobjetivo. Nesta tese, um algoritmo de estimação de distribuição híbrido, denominado HMOBEDA (Hybrid Multi-objective Bayesian Estimation of Distribution Algorithm ), o qual é baseado em redes bayesianas e busca local é proposto no contexto de otimização multi e com muitos objetivos a fim de estruturar, no mesmo modelo probabilístico, as variáveis, objetivos e as configurações dos parâmetros da busca local. Diferentes versões do HMOBEDA foram testadas utilizando instâncias do problema da mochila multiobjetivo com dois a cinco e oito objetivos. O HMOBEDA também é comparado com outros cinco métodos evolucionários (incluindo uma versão modificada do NSGA-III, adaptada para otimização combinatória) nas mesmas instâncias do problema da mochila, bem como, em um conjunto de instâncias do modelo MNK-landscape para dois, três, cinco e oito objetivos. As fronteiras de Pareto aproximadas também foram avaliadas utilizando as probabilidades estimadas pelas estruturas das redes resultantes, bem como, foram analisadas as interações entre variáveis, objetivos e parâmetros de busca local a partir da representação da rede bayesiana. Os resultados mostram que a melhor versão do HMOBEDA apresenta um desempenho superior em relação às abordagens comparadas. O algoritmo não só fornece os melhores valores para os indicadores de hipervolume, capacidade e distância invertida geracional, como também apresenta um conjunto de soluções com alta diversidade próximo à fronteira de Pareto estimada.<br>Nowadays, a number of metaheuristics have been developed for dealing with multiobjective optimization problems. Estimation of distribution algorithms (EDAs) are a special class of metaheuristics that explore the decision variable space to construct probabilistic models from promising solutions. The probabilistic model used in EDA captures statistics of decision variables and their interdependencies with the optimization problem. Moreover, the aggregation of local search methods can notably improve the results of multi-objective evolutionary algorithms. Therefore, these hybrid approaches have been jointly applied to multi-objective problems. In this work, a Hybrid Multi-objective Bayesian Estimation of Distribution Algorithm (HMOBEDA), which is based on a Bayesian network, is proposed to multi and many objective scenarios by modeling the joint probability of decision variables, objectives, and configuration parameters of an embedded local search (LS). We tested different versions of HMOBEDA using instances of the multi-objective knapsack problem for two to five and eight objectives. HMOBEDA is also compared with five cutting edge evolutionary algorithms (including a modified version of NSGA-III, for combinatorial optimization) applied to the same knapsack instances, as well to a set of MNK-landscape instances for two, three, five and eight objectives. An analysis of the resulting Bayesian network structures and parameters has also been carried to evaluate the approximated Pareto front from a probabilistic point of view, and also to evaluate how the interactions among variables, objectives and local search parameters are captured by the Bayesian networks. Results show that HMOBEDA outperforms the other approaches. It not only provides the best values for hypervolume, capacity and inverted generational distance indicators in most of the experiments, but it also presents a high diversity solution set close to the estimated Pareto front.
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Aminbakhsh, Saman. "Hybrid Particle Swarm Optimization Algorithm For Obtaining Pareto Front Of Discrete Time-cost Trade-off Problem." Master's thesis, METU, 2013. http://etd.lib.metu.edu.tr/upload/12615398/index.pdf.
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In pursuance of decreasing costs, both the client and the contractor would strive to speed up the construction project. However, accelerating the project schedule will impose additional cost and might be profitable up to a certain limit. Paramount for construction management, analyses of this trade-off between duration and cost is hailed as the time-cost trade-off (TCT) optimization. Inadequacies of existing commercial software packages for such analyses tied with eminence of discretization, motivated development of different paradigms of particle swarm optimizers (PSO) for three extensions of discrete TCT problems (DTCTPs). A sole-PSO algorithm for concomitant minimization of time and cost is proposed which involves minimal adjustments to shift focus to the completion deadline problem. A hybrid model is also developed to unravel the time-cost curve extension of DCTCPs. Engaging novel principles for evaluation of cost-slopes, and pbest/gbest positions, the hybrid SAM-PSO model combines complementary strengths of overhauled versions of the Siemens Approximation Method (SAM) and the PSO algorithm. Effectiveness and efficiency of the proposed algorithms are validated employing instances derived from the literature.
Throughout computational experiments, mixed integer programming technique is implemented to introduce the optimal non-dominated fronts of two specific benchmark problems for the very first time in the literature. Another chief contribution of this thesis can be depicted as potency of SAM-PSO model in locating the entire Pareto fronts of the practiced instances, within acceptable time-frames with reasonable deviations from the optima. Possible further improvements and applications of SAM-PSO model are suggested in the conclusion.
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Singh, Vinay. "Design and Shape Optimization of Unmanned, Semi-Rigid Airship for Rapid Descent Using Hybrid Genetic Algorithm." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/38673.
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Airships provide an eco-friendly and cost-effective means to suit sustained airborne operations. Smaller autonomous airships are highly susceptible to adverse atmospheric conditions owing to their under-actuated, underpowered and bulky size relative to other types of unmanned aerial vehicles (UAVs). To mitigate these limitations, careful considerations of the size and shape must be made at the design stage. This research presents a methodology for obtaining an optimized shape of a semi-rigid airship. Rapid descent of the LTA ship is achieved by means of a moving gondola attached to a rigid keel mounted under the helium envelope from the bow to the mid-section of the hull. The study entails the application of a robust hybrid genetic algorithm (HGA) for the multi-disciplinary design and optimization of an airship capable of rapid descent, with lower drag and optimum surface area. A comprehensive sensitivity analysis was also performed on the basis of algorithmic parameters and atmospheric conditions. With the help of HGA, a semi-rigid airship capable of carrying a payload of 0.25 kg to 1.0 kg and capable of pitching at right angles is conceptually designed. The algorithm is also tested on commercially available vehicles to validate the results. In multi-objective optimization problems (MOOPs), the significance of different objectives is dependent on the user.
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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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Jiang, Siyu. "A Comparison of PSO, GA and PSO-GA Hybrid Algorithms for Model-based Fuel Economy Optimization of a Hybrid-Electric Vehicle." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu156612591067731.
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Husk, Evan. "Imitating individualized facial expressions in a human-like avatar through a hybrid particle swarm optimization - tabu search algorithm." Honors in the Major Thesis, University of Central Florida, 2012. http://digital.library.ucf.edu/cdm/ref/collection/ETH/id/567.
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This thesis describes a machine learning method for automatically imitating a particular person's facial expressions in a human-like avatar through a hybrid Particle Swarm Optimization - Tabu Search algorithm. The muscular structures of the facial expressions are measured by Ekman and Friesen's Facial Action Coding System (FACS). Using a neutral face as a reference, the minute movements of the Action Units, used in FACS, are automatically tracked and mapped onto the avatar using a hybrid method. The hybrid algorithm is composed of Kennedy and Eberhart's Particle Swarm Optimization algorithm (PSO) and Glover's Tabu Search (TS). Distinguishable features portrayed on the avatar ensure a personalized, realistic imitation of the facial expressions. To evaluate the feasibility of using PSO-TS in this approach, a fundamental proof-of-concept test is employed on the system using the OGRE avatar. This method is analyzed in-depth to ensure its proper functionality and evaluate its performance compared to previous work.<br>B.S.P.E.<br>Bachelors<br>Engineering and Computer Science<br>Computer Engineering
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Adegbindin, Moustaine Kolawole Agnide. "Control Power Optimization using Artificial Intelligence for Forward Swept Wing and Hybrid Wing Body Aircraft." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/74950.
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Many futuristic aircraft such as the Hybrid Wing Body have numerous control surfaces that can result in large hinge moments, high actuation power demands, and large actuator forces/moments. Also, there is no unique relationship between control inputs and the aircraft response. Distinct sets of control surface deflections may result in the same aircraft response, but with large differences in actuation power. An Artificial Neural Network and a Genetic Algorithm were used here for the control allocation optimization problem of a Hybrid Wing Body to minimize the Sum of Absolute Values of Hinge Moments for a 2.5-G pull-up maneuver. To test the versatility of the same optimization process for different aircraft configurations, the present work also investigates its application on the Forward Swept Wing aircraft. A method to improve the robustness of the process is also presented. Constraints on the load factor and longitudinal pitch rate were added to the optimization to preserve the trim constraints on the control deflections. Another method was developed using stability derivatives. This new method provided better results, and the computational time was reduced by two orders of magnitude. A hybrid scheme combining both methods was also developed to provide a real-time estimate of the optimum control deflection schedules to trim the airplane and minimize the actuation power for changing flight conditions (Mach number, altitude and load factor) in a pull-up maneuver. Finally, the stability derivatives method and the hybrid scheme were applied for an antisymmetric, steady roll maneuver.<br>Master of Science
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Gupta, Shobhit. "Look-Ahead Optimization of a Connected and Automated 48V Mild-Hybrid Electric Vehicle." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1554478434629481.
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Sherbaf, Behtash Mohammad. "A Decomposition-based Multidisciplinary Dynamic System Design Optimization Algorithm for Large-Scale Dynamic System Co-Design." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1535468984437623.
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Qureshi, Muhammad Asim. "An Implementation of Ant Colony and Genetic Algorithm based Hybrid-Metaheuristic for Cut-off Grade Optimization in Open-Pit Mining Operations." Thesis, Curtin University, 2017. http://hdl.handle.net/20.500.11937/68406.
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This study presents a new mathematical model for cut-off grade and production scheduling optimisation in open pit mining operations. The model maximises the net present value of the operation subject to the mining precedence, production capacity, and grade-blending constraints. A solution using exact method establishes the computational complexity of the model. Consequently, a hybrid-metaheuristic is developed to solve practical instances of the model. Performance evaluation reflects an acceptable gap between the exact and heuristic solutions.
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Labidi, Mohamed Khalil. "Parallelisation of hybrid metaheuristics for COP solving." Thesis, Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLED029/document.
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L’Optimisation Combinatoire (OC) est un domaine de recherche qui est en perpétuel changement. Résoudre un problème d’optimisation combinatoire (POC) consiste essentiellement à trouver la ou les meilleures solutions dans un ensemble des solutions réalisables appelé espace de recherche qui est généralement de cardinalité exponentielle en la taille du problème. Pour résoudre des POC, plusieurs méthodes ont été proposées dans la littérature. On distingue principalement les méthodes exactes et les méthodes d’approximation. Ne pouvant pas viser une résolution exacte de problèmes NP-Complets lorsque la taille du problème dépasse une certain seuil, les chercheurs on eu de plus en plus recours, depuis quelques décennies, aux algorithmes dits hybrides (AH) ou encore à au calcul parallèle. Dans cette thèse, nous considérons la classe POC des problèmes de conception d'un réseau fiable. Nous présentons un algorithme hybride parallèle d'approximation basé sur un algorithme glouton, un algorithme de relaxation Lagrangienne et un algorithme génétique, qui produit des bornes inférieure et supérieure pour les formulations à base de flows. Afin de valider l'approche proposée, une série d'expérimentations est menée sur plusieurs applications: le Problème de conception d'un réseau k-arête-connexe avec contrainte de borne (kHNDP) avec L=2,3, le problème de conception d'un réseau fiable Steiner k-arête-connexe (SkESNDP) et ensuite deux problèmes plus généraux, à savoir le kHNDP avec L >= 2 et le problème de conception d'un réseau fiable k-arête-connexe (kESNDP). L'étude expérimentale de la parallélisation est présentée après cela. Dans la dernière partie de ce travail, nous présentons deux algorithmes parallèles exactes: un Branch-and-Bound distribué et un Branch-and-Cut distribué. Une série d'expérimentation a été menée sur une grappe de 128 processeurs, et des accélération intéressantes ont été atteintes pour la résolution du problèmes kHNDP avec k=3 et L=3<br>Combinatorial Optimization (CO) is an area of research that is in a constant progress. Solving a Combinatorial Optimization Problem (COP) consists essentially in finding the best solution (s) in a set of feasible solutions called a search space that is usually exponential in cardinality in the size of the problem. To solve COPs, several methods have been proposed in the literature. A distinction is made mainly between exact methods and approximation methods. Since it is not possible to aim for an exact resolution of NP-Complete problems when the size of the problem exceeds a certain threshold, researchers have increasingly used Hybrid (HA) or parallel computing algorithms in recent decades. In this thesis we consider the COP class of Survivability Network Design Problems. We present an approximation parallel hybrid algorithm based on a greedy algorithm, a Lagrangian relaxation algorithm and a genetic algorithm which produces both lower and upper bounds for flow-based formulations. In order to validate the proposed approach, a series of experiments is carried out on several applications: the k-Edge-Connected Hop-Constrained Network Design Problem (kHNDP) when L = 2,3, The problem of the Steiner k-Edge-Connected Network Design Problem (SkESNDP) and then, two more general problems namely the kHNDP when L >= 2 and the k-Edge-Connected Network Design Problem (kESNDP). The experimental study of the parallelisation is presented after that. In the last part of this work, we present a two parallel exact algorithms: a distributed Branch-and-Bound and a distributed Branch-and-Cut. A series of experiments has been made on a cluster of 128 processors and interesting speedups has been reached in kHNDP resolution when k=3 and L=3
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Chen, Minghan. "Stochastic Modeling and Simulation of Multiscale Biochemical Systems." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/90898.
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Numerous challenges arise in modeling and simulation as biochemical networks are discovered with increasing complexities and unknown mechanisms. With the improvement in experimental techniques, biologists are able to quantify genes and proteins and their dynamics in a single cell, which calls for quantitative stochastic models for gene and protein networks at cellular levels that match well with the data and account for cellular noise.
This dissertation studies a stochastic spatiotemporal model of the Caulobacter crescentus cell cycle. A two-dimensional model based on a Turing mechanism is investigated to illustrate the bipolar localization of the protein PopZ. However, stochastic simulations are often impeded by expensive computational cost for large and complex biochemical networks. The hybrid stochastic simulation algorithm is a combination of differential equations for traditional deterministic models and Gillespie's algorithm (SSA) for stochastic models. The hybrid method can significantly improve the efficiency of stochastic simulations for biochemical networks with multiscale features, which contain both species populations and reaction rates with widely varying magnitude. The populations of some reactant species might be driven negative if they are involved in both deterministic and stochastic systems. This dissertation investigates the negativity problem of the hybrid method, proposes several remedies, and tests them with several models including a realistic biological system.
As a key factor that affects the quality of biological models, parameter estimation in stochastic models is challenging because the amount of empirical data must be large enough to obtain statistically valid parameter estimates. To optimize system parameters, a quasi-Newton algorithm for stochastic optimization (QNSTOP) was studied and applied to a stochastic budding yeast cell cycle model by matching multivariate probability distributions between simulated results and empirical data. Furthermore, to reduce model complexity, this dissertation simplifies the fundamental cooperative binding mechanism by a stochastic Hill equation model with optimized system parameters. Considering that many parameter vectors generate similar system dynamics and results, this dissertation proposes a general α-β-γ rule to return an acceptable parameter region of the stochastic Hill equation based on QNSTOP. Different objective functions are explored targeting different features of the empirical data.<br>Doctor of Philosophy<br>Modeling and simulation of biochemical networks faces numerous challenges as biochemical networks are discovered with increased complexity and unknown mechanisms. With improvement in experimental techniques, biologists are able to quantify genes and proteins and their dynamics in a single cell, which calls for quantitative stochastic models, or numerical models based on probability distributions, for gene and protein networks at cellular levels that match well with the data and account for randomness. This dissertation studies a stochastic model in space and time of a bacterium’s life cycle— Caulobacter. A two-dimensional model based on a natural pattern mechanism is investigated to illustrate the changes in space and time of a key protein population. However, stochastic simulations are often complicated by the expensive computational cost for large and sophisticated biochemical networks. The hybrid stochastic simulation algorithm is a combination of traditional deterministic models, or analytical models with a single output for a given input, and stochastic models. The hybrid method can significantly improve the efficiency of stochastic simulations for biochemical networks that contain both species populations and reaction rates with widely varying magnitude. The populations of some species may become negative in the simulation under some circumstances. This dissertation investigates negative population estimates from the hybrid method, proposes several remedies, and tests them with several cases including a realistic biological system. As a key factor that affects the quality of biological models, parameter estimation in stochastic models is challenging because the amount of observed data must be large enough to obtain valid results. To optimize system parameters, the quasi-Newton algorithm for stochastic optimization (QNSTOP) was studied and applied to a stochastic (budding) yeast life cycle model by matching different distributions between simulated results and observed data. Furthermore, to reduce model complexity, this dissertation simplifies the fundamental molecular binding mechanism by the stochastic Hill equation model with optimized system parameters. Considering that many parameter vectors generate similar system dynamics and results, this dissertation proposes a general α-β-γ rule to return an acceptable parameter region of the stochastic Hill equation based on QNSTOP. Different optimization strategies are explored targeting different features of the observed data.
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Furuhashi, Takeshi, Tomohiro Yoshikawa, and Fumiya Kudo. "A Study on Analysis of Design Variables in Pareto Solutions for Conceptual Design Optimization Problem of Hybrid Rocket Engine." IEEE, 2011. http://hdl.handle.net/2237/20699.
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Pokštas, Jonas. "Pjaustymo uždavinio algoritmų realizacija ir tyrimas." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2007. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2007~D_20070816_144142-98749.
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Šiame darbe nagrinėjama negiljotininio, dvimačio, stačiakampių pjaustymo uždavinio atliekų minimizavimo problema ir jos sprendimo metodai. Dėl uždavinio kombinatorinio sudėtingumo neįmanoma tiksliai ir visais atvejais pateikti optimalų jo sprendinį, todėl pasirinkti apytiksliai sprendimo metodai. Uždavinys sprendžiamas metaeuristiniais hibridiniais genetiniu ir modeliuojamo atkaitinimo algoritmais apjungtais su euristiniais „Žemiausio kairėn užpildymo“ ir „Žemiausio tarpo“, kuris yra originali „Geriausiai tinkamo“ metodo modifikacija. Taip pat realizuojami minėti euristiniai algoritmai atskirai nuo hibridinių. Atliekama šių metodų lyginamoji analizė bei jų parametrų ir pradinių sąlygų parinkimo įtakos tyrimas sprendinio kokybei. Suformuojama ir pateikiama metodika pjaustymo uždavinių sprendimui.<br>A non – guillotinable, two – dimensional, rectangular cutting stock problem is being introduced in this paper and its solving methods either. Due to the combinatorial complexity of a problem, it is impossible to solve it optimally for every instance. Consequently an aproximate methods have been chosen. The problem is solved by metaheuristic genetic and simulated annealing methods hybridised with heuristic „Bottom Left Fill“ and „Lowest Gap“, which is an originally modified version of „Best Fit“ algorithm. The same heuristic algorithms are implemented separately from hybridised ones. A comparation analysis of these methods is done and the influence on solution quality depending on the selection of algorithms parameters and its initial conditions is considered. The methodology of solving cutting stock problems is being formulated and presented.
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Zemzami, Maria. "Variations sur PSO : approches parallèles, jeux de voisinages et applications Application d’un modèle parallèle de la méthode PSO au problème de transport d’électricité A modified Particle Swarm Optimization algorithm linking dynamic neighborhood topology to parallel computation An evolutionary hybrid algorithm for complex optimization problems Interoperability optimization using a modified PSO algorithm A comparative study of three new parallel models based on the PSO algorithm Optimization in collaborative information systems for an enhanced interoperability network." Thesis, Normandie, 2019. http://www.theses.fr/2019NORMIR11.
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Reconnue depuis de nombreuses années comme une méthode efficace pour la résolution de problèmes difficiles, la méta-heuristique d’optimisation par essaim de particules PSO (Particle Swarm Optimization) présente toutefois des inconvénients dont les plus étudiés sont le temps de calcul élevé et la convergence prématurée. Cette thèse met en exergue quelques variantes de la méthode PSO visant à échapper à ces deux inconvénients de la méthode. Ces variantes combinent deux approches : la parallélisation de la méthode de calcul et l’organisation de voisinages appropriés pour les particules. L’évaluation de la performance des modèles proposés a été effectuée sur la base d'une expérimentation sur une série de fonctions tests. A la lumière de l’analyse des résultats expérimentaux obtenus, nous observons que les différents modèles proposés donnent des résultats meilleurs que ceux du PSO classique en termes de qualité de la solution et du temps de calcul. Un modèle basé PSO a été retenu et développé en vue d'une expérimentation sur le problème du transport d’électricité. Une variante hybride de ce modèle avec la méthode du recuit simulé SA (Simulated Annealing) a été considérée et expérimentée sur la problématique des réseaux de collaboration<br>Known for many years as a stochastic metaheuristic effective in the resolution of difficult optimization problems, the Particle Swarm Optimization (PSO) method, however, shows some drawbacks, the most studied: high running time and premature convergence. In this thesis we consider some variants of the PSO method to escape these two disadvantages. These variants combine two approaches: the parallelization of the calculation and the organization of appropriate neighborhoods for the particles. To prove the performance of the proposed models, we performed an experiment on a series of test functions. By analyzing the obtained experimental results, we observe that the proposed models based on the PSO algorithm performed much better than basic PSO in terms of computing time and solution quality. A model based on the PSO algorithm was selected and developed for an experiment on the problem of electricity transmission. A hybrid variant of this model with Simulated Annealing (SA) algorithm has been considered and tested on the problem of collaborative networks
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GARRAFFA, MICHELE. "Exact and Heuristic Hybrid Approaches for Scheduling and Clustering Problems." Doctoral thesis, Politecnico di Torino, 2016. http://hdl.handle.net/11583/2639115.
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This thesis deals with the design of exact and heuristic algorithms for scheduling and clustering combinatorial optimization problems. All the works are linked by the fact that all the presented methods arebasically hybrid algorithms, that mix techniques used in the world of combinatorial optimization. The algorithms are all efficient in practice, but the one presented in Chapter 4, that has mostly theoretical
interest. Chapter 2 presents practical solution algorithms based on an ILP model for an energy scheduling combinatorial problem that arises in a smart building context. Chapter 3 presents a new cutting stock problem
and introduce a mathematical formulation and a heuristic solution approach based on a heuristic column generation scheme. Chapter 4 provides an exact exponential algorithm, whose importance is only theoretical so far, for a classical scheduling problem: the Single Machine Total Tardiness Problem. The relevant aspect is that the designed algorithm has the best worst case complexity for the problem, that has been studied for several decades. Furthermore, such result is based on a new technique, called Branch and Merge, that avoids the solution of several equivalent sub-problems in a branching algorithm that requires polynomial space. As a consequence, such technique embeds in a branching algorithm ideas coming from other traditional computer science techniques such as dynamic programming and memorization, but keeping the space requirement polynomial.
Chapter 5 provides an exact approach based on semidefinite programming and a matheuristic approach based on a quadratic solver for a fractional clustering combinatorial optimization problem, called Max-Mean Dispersion Problem. The matheuristic approach has the peculiarity of using a non-linear MIP solver. The proposed exact approach uses a general semidefinite programming relaxation and it is likely to be extended to other combinatorial problems with a fractional formulation.
Chapter 6 proposes practical solution methods for a real world clustering problem arising in a smart city context. The solution algorithm is based on the solution of a Set Cover model via a commercial ILP solver.
As a conclusion, the main contribution of this thesis is given by several approaches of practical or theoretical interest, for two classes of important combinatorial problems: clustering and scheduling. All the practical methods presented in the thesis are validated by extensive computational experiments, that compare the proposed methods with the ones available in the state of the art.
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Babatunde, Oluleye Hezekiah. "A neuro-genetic hybrid approach to automatic identification of plant leaves." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2015. https://ro.ecu.edu.au/theses/1733.
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Plants are essential for the existence of most living things on this planet. Plants are used for providing food, shelter, and medicine. The ability to identify plants is very important for several applications, including conservation of endangered plant species, rehabilitation of lands after mining activities and differentiating crop plants from weeds.
In recent times, many researchers have made attempts to develop automated plant species recognition systems. However, the current computer-based plants recognition systems have limitations as some plants are naturally complex, thus it is difficult to extract and represent their features. Further, natural differences of features within the same plant and similarities between plants of different species cause problems in classification.
This thesis developed a novel hybrid intelligent system based on a neuro-genetic model for automatic recognition of plants using leaf image analysis based on novel approach of combining several image descriptors with Cellular Neural Networks (CNN), Genetic Algorithm (GA), and Probabilistic Neural Networks (PNN) to address classification challenges in plant computer-based plant species identification using the images of plant leaves.
A GA-based feature selection module was developed to select the best of these leaf features. Particle Swam Optimization (PSO) and Principal Component Analysis (PCA) were also used sideways for comparison and to provide rigorous feature selection and analysis. Statistical analysis using ANOVA and correlation techniques confirmed the effectiveness of the GA-based and PSO-based techniques as there were no redundant features, since the subset of features selected by both techniques correlated well. The number of principal components (PC) from the past were selected by conventional method associated with PCA. However, in this study, GA was used to select a minimum number of PC from the original PC space. This reduced computational cost with respect to time and increased the accuracy of the classifier used. The algebraic nature of the GA’s fitness function ensures good performance of the GA. Furthermore, GA was also used to optimize the parameters of a CNN (CNN for image segmentation) and then uniquely combined with PNN to improve and stabilize the performance of the classification system. The CNN (being an ordinary differential equation (ODE)) was solved using Runge-Kutta 4th order algorithm in order to minimize descritisation errors associated with edge detection.
This study involved the extraction of 112 features from the images of plant species found in the Flavia dataset (publically available) using MATLAB programming environment. These features include Zernike Moments (20 ZMs), Fourier Descriptors (21 FDs), Legendre Moments (20 LMs), Hu 7 Moments (7 Hu7Ms), Texture Properties (22 TP) , Geometrical Properties (10 GP), and Colour features (12 CF). With the use of GA, only 14 features were finally selected for optimal accuracy. The PNN was genetically optimized to ensure optimal accuracy since it is not the best practise to fix the tunning parameters for the PNN arbitrarily. Two separate GA algorithms were implemented to optimize the PNN, that is, the GA provided by MATLAB Optimization Toolbox (GA1) and a separately implemented GA (GA2). The best chromosome (PNN spread) for GA1 was 0.035 with associated classification accuracy of 91.3740% while a spread value of 0.06 was obtained from GA2 giving rise to improved classification accuracy of 92.62%. The PNN-based classifier used in this study was benchmarked against other classifiers such as Multi-layer perceptron (MLP), K Nearest Neigbhour (kNN), Naive Bayes Classifier (NBC), Radial Basis Function (RBF), Ensemble classifiers (Adaboost). The best candidate among these classifiers was the genetically optimized PNN. Some computational theoretic properties on PNN are also presented.
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Ni, Marcus. "Automated Hybrid Singularity Superposition and Anchored Grid Pattern BEM Algorithm for the Solution of the Inverse Geometric Problem." Master's thesis, University of Central Florida, 2013. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5827.
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A method for solving the inverse geometrical problem is presented by reconstructing the unknown subsurface cavity geometry using boundary element methods, a genetic algorithm, and Nelder-Mead non-linear simplex optimization. The heat conduction problem is solved utilizing the boundary element method, which calculates the difference between the measured temperature at the exposed surface and the computed temperature under the current update of the unknown subsurface flaws and cavities. In a first step, clusters of singularities are utilized to solve the inverse problem and to identify the location of the centroid(s) of the subsurface cavity(ies)/flaw(s). In a second step, the reconstruction of the estimated cavity(ies)/flaw(s) geometry(ies) is accomplished by utilizing an anchored grid pattern upon which cubic spline knots are restricted to move in the search for unknown geometry. Solution of the inverse problem is achieved using a genetic algorithm accelerated with the Nelder-Mead non-linear simplex. To optimize the cubic spline interpolated geometry, the flux (Neumann) boundary conditions are minimized using a least squares functional. The automated algorithm successfully reconstructs single and multiple subsurface cavities within two dimensional mediums. The solver is also shown to accurately predict cavity geometries with random noise in the boundary condition measurements. Subsurface cavities can be difficult to detect based on their location. By applying different boundary conditions to the same geometry, more information is supplied at the boundary, and the subsurface cavity is easily detected despite its low heat signature effect at the boundaries. Extensions to three-dimensional applications are outlined.<br>M.S.M.E.<br>Masters<br>Mechanical and Aerospace Engineering<br>Engineering and Computer Science<br>Mechanical Engineering; Thermo-Fluids
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Kingry, Nathaniel. "Heuristic Optimization and Sensing Techniques for Mission Planning of Solar-Powered Unmanned Ground Vehicles." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1523874767812408.
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Hasda, Ranjan Kumar. "Contribution to the optimization of Unequal Area Rectangular Facility Layout Problem." Thesis, Ecole centrale de Nantes, 2017. http://www.theses.fr/2017ECDN0026.
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L'agencement d'espace est un problème courant dans la plupart des secteurs industriels. Ce problème est de nature continue et discret et il est considéré comme un problème NP-difficile. Les méthodes d'optimisation traditionnelles, plus appropriées pour une recherche locale sont difficilement utilisables aux problèmes d'agencement. Afin de contourner ces limitations inhérentes aux méthodes classiques, nous proposons deux algorithmes adaptés aux problèmes d'agencement statique de composants de différentes tailles. Pour les problèmes d'agencement considérés, les fonctions objectives à minimiser sont non linéaires et représentent les coûts associés aux sommes pondérées des distances entre les composants. La première approche que nous considérons est une méthode hybride en deux étapes. La première étape consiste à construire un agencement en se basant sur la méthode dite "bas-gauche" comme une solution locale. Ensuite, la solution obtenue est améliorée en appliquant un algorithme génétique modifié. Les opérateurs de croisement et de mutation sont alors adaptés pour prendre en compte les spécificités du problème d'agencement. La deuxième approche est une combinaison entre une recherche locale et globale. Dans ce cas, l'algorithme génétique est également modifié par l'introduction d'un opérateur spécialisé pour le traitement des rotations des composants. Il permet notamment d'éviter le couplage entre les variables réelles et entières et permet également de réduire considérablement le nombre de variables du problème d'optimisation. Les performances des deux approches sont testées et comparées avec les exemples de référence extraits des publications traitant du problème d'optimisation d'agencement. Nous démontrons que les deux approches que nous proposons obtiennent de meilleures performances que les approches existantes<br>A facility layout design is one of the most commonly faced problems in the manufacturing sectors. The problem is mixed-integer in nature and usually an NP-hard problem, which makes it difficult to solve using classical optimization techniques, which are better for local search. To overcome these limitations, two algorithms have been proposed for solving static facility layout problems with the unequal size compartments. The objective function of the problems considered is nonlinear in which the sum of the material handling cost has been minimized. In the first approach, a hybrid constructive and improvement model has been proposed where an advanced bottom-left fill technique was used as constructive approach. The constructive model proposed also acts as a local search method based on greedy algorithm. For improvement approach a hybrid genetic algorithm has been proposed, where the crossover and mutation operator are specially designed to handle the solution representation which itself is used as constructive model. In the second approach, a combined local and global search model was proposed where a rotation operator was used to avoid mixed-integer formulation of the problem. Use of rotation operator has also reduced the number of variables significantly. Apart from the conventional evolutionary operators this model has also used exchange and rotation operators. The performances of both model are tested over a previously solved problem selected from the literature. The evaluation of the results shows that the performances of the proposed models are better than many existing algorithms and has the potential for field applications
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Jin, Yan. "Hybrid metaheuristic algorithms for sum coloring and bandwidth coloring." Thesis, Angers, 2015. http://www.theses.fr/2015ANGE0062/document.
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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 analysés<br>The 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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Abdussalam, Fathi M. A. "Antenna design using optimization techniques over various computaional electromagnetics. Antenna design structures using genetic algorithm, Particle Swarm and Firefly algorithms optimization methods applied on several electromagnetics numerical solutions and applications including antenna measurements and comparisons." Thesis, University of Bradford, 2018. http://hdl.handle.net/10454/17217.
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Dealing with the electromagnetic issue might bring a sort of discontinuous and nondifferentiable
regions. Thus, it is of great interest to implement an appropriate optimisation
approach, which can preserve the computational resources and come up with a global
optimum. While not being trapped in local optima, as well as the feasibility to overcome some
other matters such as nonlinear and phenomena of discontinuous with a large number of
variables.
Problems such as lengthy computation time, constraints put forward for antenna
requirements and demand for large computer memory, are very common in the analysis due
to the increased interests in tackling high-scale, more complex and higher-dimensional
problems. On the other side, demands for even more accurate results always expand
constantly. In the context of this statement, it is very important to find out how the recently
developed optimization roles can contribute to the solution of the aforementioned problems.
Thereafter, the key goals of this work are to model, study and design low profile antennas for
wireless and mobile communications applications using optimization process over a
computational electromagnetics numerical solution. The numerical solution method could be
performed over one or hybrid methods subjective to the design antenna requirements and
its environment.
Firstly, the thesis presents the design and modelling concept of small uni-planer Ultra-
Wideband antenna. The fitness functions and the geometrical antenna elements required for
such design are considered. Two antennas are designed, implemented and measured. The
computed and measured outcomes are found in reasonable agreement. Secondly, the work
is also addressed on how the resonance modes of microstrip patches could be performed
using the method of Moments. Results have been shown on how the modes could be
adjusted using MoM. Finally, the design implications of balanced structure for mobile
handsets covering LTE standards 698-748 MHz and 2500-2690 MHz are explored through
using firefly algorithm method. The optimised balanced antenna exhibits reasonable
matching performance including near-omnidirectional radiations over the dual desirable
operating bands with reduced EMF, which leads to a great immunity improvement towards
the hand-held.<br>General Secretariat of Education and Scientific Research Libya
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Lu, Zhi. "Optimization approaches for minimum conductance graph partitioning." Thesis, Angers, 2020. http://www.theses.fr/2020ANGE0013.
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Le problème de partitionnement de graphe de conductance minimale (MCGPP) est un problème d’optimisation combinatoire NP-difficile avec de nombreuses applications pratiques dans divers domaines tels que la détection communautaire, la bioinformatique et la vision par ordinateur. Etant donnée sa complexité intrinsèque, des approches heuristiques et métaheuristiques constituent un moyen convenable pour résoudre des instances de grande taille. Cette thèse est consacrée au développement d’algorithmes métaheuristiques performants pour le MC-GPP. Plus précisément, nous proposons un algorithme «Stagnation aware Breakout Tabu Search», un algorithme évolutif hybride (MAMC) et un algorithme multiniveaubasé sur le recuit simulé (IMSA). Nous présentons des résultats expérimentaux sur de nombreux graphes de grande dimension de la littérature ayant jusqu’à 23 millions de sommets. Nous montrons la haute performance de nos algorithmes par rapport à l’état de l’art. Nous analysons les éléments algorithmiques et stratégies de recherche pour mettre en lumière leur influence sur la performance des algorithmes proposés<br>The minimum conductance graph partitioning problem (MC-GPP) is an important NP-hard combinatorial optimization problem with numerous practical applications in various areas such as community detection, bioinformatics, and computer vision. Due to its high computational complexity, heuristic and metaheuristic approaches constitute a highly useful tool for approximating this challenging problem. This thesis is devoted to developing effective metaheuristic algorithms for the MC-GPP. Specifically, we propose a stagnation-aware breakout tabu search algorithm (SaBTS), a hybrid evolutionary algorithm (MAMC), and an iterated multilevel simulated annealing algorithm (IMSA). Extensive computational experiments and comparisons on large and massive benchmark instances (with more than 23 million vertices) demonstrate that the proposed algorithms compete very favorably with stateof- the-art algorithms in the literature. Furthermore, the key issues of these algorithms are analyzed to shed light on their influences over the performance of the proposed algorithms
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Tchvagha, Zeine Ahmed. "Contribution à l’optimisation multi-objectifs sous contraintes : applications à la mécanique des structures." Thesis, Normandie, 2018. http://www.theses.fr/2018NORMIR13/document.
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L’objectif de cette thèse est le développement de méthodes d’optimisation multi-objectif pour la résolution de problèmes de conception des structures mécaniques. En effet, la plupart des problèmes réels dans le domaine de la mécanique des structures ont plusieurs objectifs qui sont souvent antagonistes. Il s’agit, par exemple, de concevoir des structures en optimisant leurs poids, leurs tailles, et leurs coûts de production. Le but des méthodes d’optimisation multi-objectif est la recherche des solutions de compromis entre les objectifs étant donné l’impossibilité de satisfaire tout simultanément. Les métaheuristiques sont des méthodes d’optimisation capables de résoudre les problèmes d’optimisation multi-objective en un temps de calcul raisonnable sans garantie de l’optimalité de (s) solution (s). Au cours des dernières années, ces algorithmes ont été appliqués avec succès pour résoudre le problème des mécaniques des structures. Dans cette thèse deux métaheuristiques ont été développées pour la résolution des problèmes d’optimisation multi-objectif en général et de conception de structures mécaniques en particulier. Le premier algorithme baptisé MOBSA utilise les opérateurs de croisement et de mutation de l’algorithme BSA. Le deuxième algorithme nommé NNIA+X est une hybridation d’un algorithme immunitaire et de trois croisements inspirés de l’opérateur de croisement original de l’algorithme BSA. Pour évaluer l’efficacité et l’efficience de ces deux algorithmes, des tests sur quelques problèmes dans littérature ont été réalisés avec une comparaison avec des algorithmes bien connus dans le domaine de l’optimisation multi-objectif. Les résultats de comparaison en utilisant des métriques très utilisées dans la littérature ont démontré que ces deux algorithmes peuvent concurrencer leurs prédécesseurs<br>The objective of this thesis is the development of multi-objective optimization methods for solving mechanical design problems. Indeed, most of the real problems in the field of mechanical structures have several objectives that are often antagonistic. For example, it is about designing structures by optimizing their weight, their size, and their production costs. The goal of multi-objective optimization methods is the search for compromise solutions between objectives given the impossibility to satisfy all simultaneously. Metaheuristics are optimization methods capable of solving multi-objective optimization problems in a reasonable calculation time without guaranteeing the optimality of the solution (s). In recent years, these algorithms have been successfully applied to solve the problem of structural mechanics. In this thesis, two metaheuristics have been developed for the resolution of multi-objective optimization problems in general and of mechanical structures design in particular. The first algorithm called MOBSA used the crossover and mutation operators of the BSA algorithm. The second one named NNIA+X is a hybridization of an immune algorithm and three crossover inspired by the original crossover operator of the BSA algorithm. To evaluate the effectiveness and efficiency of these two algorithms, tests on some problems in literature have been made with a comparison with algorithms well known in the field of multi-objective optimization. The comparison results using metrics widely used in the literature have shown that our two algorithms can compete with their predecessors
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Sahnoun, Mohamed Aymen. "Contribution à la modélisation et au contrôle de trajectoire de Trackers photovoltaïques à haute concentration (HCPV)." Thesis, Paris, ENSAM, 2015. http://www.theses.fr/2015ENAM0043/document.
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Dans une optique de maximisation de la production et de réduction des coûts d’installation, de maintenance et d’entretien des trackers solaires, qui permettent d’orienter les modules photovoltaïques à haute concentration (HCPV), ces travaux de thèse se focalisent sur l’amélioration de la précision et la réduction du coût de la stratégie de génération de la trajectoire du tracker. Dans un premier temps, un simulateur de tracker HCPV est développé offrant une étude de l’influence de la performance du suivi du soleil sur la production des modules HCPV, permettant ainsi une étude et une comparaison des stratégies de génération de trajectoires. Le simulateur est basé sur un modèle comportemental de module HCPV monté sur tracker permettant de prédire la puissance maximale du module HCPV en fonction de l’erreur de position du tracker face au soleil, de l’ensoleillement direct et de la température. Une première stratégie de commande dite de référence a été implémentée sur ce simulateur. C’est une commande hybride qui repose sur un viseur solaire pour corriger l’erreur de poursuite par un calcul astronomique. Ensuite, afin d’améliorer les performances et de réduire les coûts de cette stratégie, une nouvelle approche sans capteur est développée en se basant sur une méthode d’optimisation du gradient de puissance pour la génération de la trajectoire du tracker. Une étude complémentaire est également exposée afin de mettre en évidence des algorithmes de recherche de la puissance maximale (MPPT) pouvant offrir des temps de réponse suffisamment rapides pour ne pas affecter la qualité de l’évaluation du gradient de puissance. Dans ce contexte, une commande MPPT P&O améliorée par un réseau de neurones à complexité réduite est proposée, assurant un compromis entre précision, simplicité et rapidité<br>This work focuses on improving the accuracy and on reducing the cost of the tracker generating trajectory strategy, in order to maximize the production and to reduce the installation and the maintenance cost of a solar tracker orienting high concentrated photovoltaic modules (HCPV). Initially, we propose a behavioral modeling of the HCPV module mounted on a dual axis tracker in order to study the influence of the tracking performance on the module power production. Then, this simulator can be used to test control strategies and to compare their performance. Firstly, a classical control strategy is implemented in the simulator. It is based on a hybrid control operating an astronomical calculation to follow the sun path, and a sun sensor to correct the tracking error. A sensorless strategy is proposed in this work to reduce the cost of the HCPV tracker control. This strategy is based on a gradient optimization algorithm to generate the tracker trajectory and to catch the sun path. Tested on the simulator, this strategy presents the same accuracy as the classical strategy while being less costly. The last study proposed in this thesis work concerns maximum power point tracking (MPPT) algorithms, in order to respond to a given problem relating to the practical implementation of gradient algorithm. In this context, we propose an original optimization of the P&O MPPT control with a neural network algorithm leading to a significant reduction of the computational cost required to train it. This approach, which is ensuring a good compromise between accuracy and complexity is sufficiently fast to not affect the quality of the evaluation of the gradient
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El, Hami Norelislam. "Contribution aux méthodes hybrides d'optimisation heuristique : Distribution et application à l'interopérabilité des systèmes d'information." Phd thesis, INSA de Rouen, 2012. http://tel.archives-ouvertes.fr/tel-00771360.
Full textAbstract:
Les travaux présentés dans ce mémoire proposent une nouvelle méthode d'optimisation globale dénommée MPSO-SA. Cette méthode hybride est le résultat d'un couplage d'une variante d'algorithme par Essaim de particules nommé MPSO (Particle Swarm Optimization) avec la méthode du recuit simulé nommé SA (Simulted Annealing). Les méthodes stochastiques ont connu une progression considérable pour la résolution de problèmes d'optimisation. Parmi ces méthodes, il y a la méthode Essaim de particules (PSO° qui est développée par [Eberhart et Kennedy (1995)]. Quant à la méthode recuit simulé (SA), elle provient du processus physique qui consiste à ordonner les atomes d'un cristal afin de former une structure cristalline parfaite. Pour illustrer les performances de la méthode MPSO-SA proposée, une comparaison avec MPSO et SA est effectuée sur des fonctions tests connues dans la littérature. La métode MPSO-SA est utilisée pour la résolution des problèmes réels interopérabilité des systèmes d'information, ainsi qu'aux problèmes d'optimisation et de fiabilité des structures mécaniques.
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Shi, Yong. "Modeling and Solving Home Health Care Routing and Scheduling Problem with Consideration of Uncertainties." Thesis, Bourgogne Franche-Comté, 2018. http://www.theses.fr/2018UBFCA027.
Full textAbstract:
Les soins de santé à domicile (HHC) sont un large éventail de services de santé pouvant être dispensés à domicile pour une maladie ou une blessure. Ces dernières années, le secteur des soins de santé est devenu l'un des plus grands secteurs de l'économie des pays développés. L'un des défis les plus importants dans le domaine des HHC consiste à affecter plus efficacement les ressources en main-d'œuvre et les équipements sous des ressources limitées. Étant donné que le coût du transport est l’une des dépenses les plus critiques dans les activités de l’entreprise, il est très important d’optimiser le problème de routage des véhicules pour les sociétés HHC.Cependant, la majorité des travaux existants ne prennent en compte que le modèle déterministe. Dans la pratique de HHC, le décideur et les aidants rencontrent souvent des incertitudes. Il est donc essentiel d'intégrer l'incertitude dans le modèle pour établir un calendrier raisonnable pour la société HHC. Cette thèse aborde le problème du routage et de la planification HHC en prenant en compte respectivement la demande non déterministe, le service et le temps de parcours. Le corps principal de la thèse est composé de trois œuvres indépendantes.(1) Sur la base de la théorie de la crédibilité floue, nous avons proposé un modèle de programmation par contraintes de hasard flou (FCCP) pour le problème de routage HHC avec une demande floue. Ce modèle présente à la fois des caractéristiques d'optimisation combinatoire et de FCCP. Pour faire face au problème à grande échelle, nous avons développé un algorithme génétique hybride avec la simulation de Monte Carlo. Trois séries d'expériences ont été menées pour valider les performances du modèle et de l'algorithme proposés. Enfin, l’analyse de sensibilité a également porté sur l’observation du paramètre variable impliqué dans la prise de décision floue.(2) En fonction de l'activité des soignants de HHC, nous avons proposé un modèle de programmation stochastique en deux étapes avec recours (SPR) pour la livraison et la reprise simultanées avec des temps de trajet et de service stochastiques dans HHC. Pour résoudre le modèle, nous avons d’une part réduit le modèle au cas déterministe. Le solveur de Gurobi, le recuit simulé (SA), l’algorithme de chauve-souris, l’algorithme de luciole ont été proposés pour résoudre le modèle déterministe pour 56 instances respectivement. Enfin, le SA a été adopté pour traiter le modèle SPR. Une comparaison entre les solutions obtenues par les deux modèles a également été réalisée pour mettre en évidence la prise en compte des temps de parcours et de service stochastiques.(3) Pour garantir la qualité du service, sur la base d’un budget de la théorie de l’incertitude, nous avons proposé un modèle d’optimisation robuste (RO) pour HHC Routing, prenant en compte les exigences en termes de temps de déplacement et de service. La vérification de la solution réalisable a été réécrite en tant que fonction récursive complexe. Recherche tabou, SA, Recherche de voisinage variable sont également adaptés pour résoudre le modèle. Un grand nombre d'expériences ont été réalisées pour évaluer le modèle déterministe et le modèle RO. Une analyse de sensibilité des paramètres a également été effectuée<br>Home health care (HHC) is a wide range of healthcare services that can be given in one's home for an illness or injury. In recent years, the healthcare industry has become one of the largest sectors of the economy in developed countries. One of the most significant challenges in HHC domain is to assign the labor resources and equipment more efficiently under limited resources. Since the transportation cost is one of the most critical spendings in the company activities, it is of great significance to optimize the vehicle routing problem for HHC companies.However, a majority of the existing work only considers the deterministic model. In the practical of HHC, the decision-makers and caregivers often encounter with uncertainties. So, it is essential to incorporate the uncertainty into the model to make a reasonable and robust schedule for HHC company. This thesis addresses the HHC routing and scheduling problem with taking into account the non-deterministic demand, uncertain service and travel time respectively. The main body the thesis is composed of three independent works.(1) Based on the Fuzzy Credibility Theory, we proposed a fuzzy chance constraint programming (FCCP) model for HHC routing problem with fuzzy demand. This model has both characteristics of combinatorial optimization and FCCP. To deal with the large-scale problem, we developed a Hybrid Genetic Algorithm with the Monte Carlo simulation. Three series of experiments were conducted to validate the performance of the proposed model and algorithm. At last the sensitivity analysis was also carried out the observe the variable parameter involved in the fuzzy decision-making.(2) According to the activity of the caregivers in HHC, we proposed a two-stage stochastic programming model with recourse (SPR) for the simultaneous delivery and pick-up with stochastic travel and service times in HHC. To solve the model, firstly, we reduced the model to the deterministic one. Gurobi Solver, Simulated Annealing (SA), Bat Algorithm (BA), Firefly Algorithm (FA) were proposed to solve the deterministic model for 56 instances respectively. At last the SA was adopted to address the SPR model. Comparison between the solutions obtained by the two models was also conducted to highlight the consideration of the stochastic travel and service times.(3) To guarantee the service quality, based on a budget of uncertainty theory, we proposed a Robust Optimization (RO) model for HHC Routing with considering skill requirements under travel and service times uncertainty. The feasible solution check was rewritten as a complex recursive function. Tabu Search, SA, Variable Neighborhood Search are adapted to solve the model. A large number of experiments had been performed to evaluate the deterministic model and the RO model
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Cha, Young Jin. "Structural control Architecture Optimization for 3-D Systems Using Advanced Multi-Objective Genetic Algorithms." 2008. http://hdl.handle.net/1969.1/ETD-TAMU-2008-12-208.
Full textAbstract:
The architectures of the control devices in active control algorithm are an
important fact in civil structural buildings. Traditional research has limitations in finding
the optimal architecture of control devices such as using predefined numbers or
locations of sensors and dampers within the 2-and 3-dimensional (3-D) model of the
structure. Previous research using single-objective optimization only provides limited
data for defining the architecture of sensors and control devices. The Linear Quadratic
Gaussian (LQG) control algorithm is used as the active control strategy. The American
Society of Civil Engineers (ASCE) control benchmark building definition is used to
develop the building system model. The proposed gene manipulation genetic algorithm
(GMGA) determines the near-optimal Pareto fronts which consist of varying numbers
and locations of sensors and control devices for controlling the ASCE benchmark
building by considering multi-objectives such as interstory drift and minimizing the
number of the control devices.
The proposed GMGA reduced the central processing unit (CPU) run time and
produced more optimal Pareto fronts for the 2-D and 3-D 20-story building models. Using the GMGA provided several benefits: (1) the possibility to apply any presuggested
multi-objective optimization mechanism; (2) the availability to perform a
objective optimization problem; (3) the adoptability of the diverse encoding provided by
the GA; (4) the possibility of including the engineering judgment in generating the next
generation population by using a gene creation mechanisms; and (5) the flexibility of the
gene creation mechanism in applying and changing the mechanism dependent on
optimization problem.
The near-optimal Pareto fronts obtained offer the structural engineer a diverse
choice in designing control system and installing the control devices. The locations and
numbers of the dampers and sensors in each story are highly dependent on the sensor
locations. By providing near-Pareto fronts of possible solutions to the engineer that also
consider diverse earthquakes, the engineer can get normalized patterns of architectures
of control devices and sensors about random earthquakes.
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Shih, Yan-Chih, and 施彥池. "Optimization of Convolutional Neural Network Using Hybrid Algorithm." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/csk6t2.
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Chartniyom, Siradej. "Optimization of multiple location inventories using hybrid genetic algorithm." 2009. http://arrow.unisa.edu.au:8081/1959.8/50716.
Full textAbstract:
The thesis contributes to the body of knowledge in analyzing and optimizing inventories of multiple stocking locations in a supply chain system. Optimization model is developed for planning inventories with respect to the proposed inventory-pooling strategy. The model is solved under stochastic environment using a Hybrid Genetic Algorithm technique.
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Lo, Kuan-Chun, and 羅冠君. "A HS-DLM Hybrid Searching Algorithm for Structural Optimization." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/r4528j.
Full textAbstract:
碩士<br>國立中央大學<br>土木工程研究所<br>96<br>This report is devoted to the presentation of a hybrid meta-heuristic algorithm, namely HS-DLM, for optimum design of structures with continuous, discrete and mixed variables. The HS (Harmony Search) has the ability in performing global search. However, the main deficiencies of HS are lacking accuracy of local search and the way of dealing with constrains. To overcome these drawbacks, DLM is proposed to enhance the local search capacity of HS and repair violated constrains for the problem such that the probability of obtaining global optimum for the HS-DLM can be increased. More than ten typical structures studied in the literature were used to validate the effectiveness of the algorithm. The comparative studies of the HS-DLM against other optimization algorithms are reported to show the performance and the solution quality of the proposed HS-DLM algorithm. It shows that the performance of HS-DLM algorithm is reliable, and the solution quality of the optimum structural design problems studied in the literature is comparable to other meta-heuristic methods.
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Strite, Lisa. "A hybrid ant colony optimization algorithm for graph bisection /." 2001. http://emp3.hbg.psu.edu/theses/available/etd-12202001-102921/.
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Chen, You-Yu, and 陳攸伃. "The Development of Hybrid Optimization Algorithm for Fuzzy Clustering." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/t937sf.
Full textAbstract:
碩士<br>國立臺北科技大學<br>工業工程與管理研究所<br>97<br>The Fuzzy C-means Algorithm as proposed by Dunn (1974) is a commonly used fuzzy clustering method which conducts data clustering by randomly selecting initial centroids. With larger data size or attribute dimensions, clustering results may be affected and more repetitive computations are required. To compensate the effect of random initial centroids on results, this study proposed a hybrid optimization algorithm－Genetic Immune Fuzzy C-means Algorithm (GIFA). This algorithm first obtains the proper initial cluster centroids and then cluster data to improve clustering efficiency. And tests GIFA through three data sets: Teaching Assistant Evaluation, Ecoli and Class Identification, and compares the results with the executed results of Fuzzy C-means Algorithm (FCM), Genetic Fuzzy C-means Algorithm (GFA), and Immune Fuzzy C-means Algorithm (IFA). Analyze the advantages and disadvantages of the algorithms by convergence value of objective function and convergence iterations. The results suggest that GIFA could achieve better clustering results.
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Sahab, M. G., Ashraf F. Ashour, and V. V. Toropov. "A Hybrid Genetic Algorithm for Reinforced Concrete Flat Slab." 2009. http://hdl.handle.net/10454/3181.
Full textAbstract:
No<br>This paper presents a two-stage hybrid optimization algorithm based on a modified genetic algorithm. In the first stage, a global search is carried out over the design search space using a modified GA. The proposed modifications on the basic GA includes dynamically changing the population size throughout the GA process and the use of different forms of the penalty function in constraint handling. In the second stage, a local search based on the genetic algorithm solution is executed using a discretized form of Hooke and Jeeves method.
The hybrid algorithm and the modifications to the basic genetic algorithm are examined on the design optimization of reinforced concrete flat slab buildings. The objective function is the total cost of the structure including the cost of concrete, formwork, reinforcement and foundation excavation. The constraints are defined according to the British Standard BS8110 for reinforced concrete structures. Comparative studies are presented to study the effect of different parameters of handling genetic algorithm on the optimized flat slab building. It has been shown that the proposed hybrid algorithm can improve genetic algorithm solutions at the expense of more function evaluations.
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Chuang, Wen-Shan, and 莊玟珊. "A PSO-SA Hybrid Searching Algorithm for Optimization of Structures." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/75048492352353983350.
Full textAbstract:
碩士<br>國立中央大學<br>土木工程研究所<br>95<br>This report is devoted to the presentation of two hybrid search
algorithms, namely PSO–SA–Pg and PSO–SA–Pi, for optimum design of
structures with continuous, discrete and mixed variables. The PSO
(Particle Swarm Optimization) is an evolutionary computation technique
which has ability in performing global search. The main deficiency of
PSO is that all particles have the tendency to fly to the current best
solution which may be a local optimum or a solution near local optimum.
In this case, all particles will move toward to a small region and the
global exploration ability will be weakened. To overcome the drawback
of premature convergence of the method and to make the algorithm
explore the local and global minima thoroughly at the same time, two
hybrid search algorithms are proposed. More than ten typical structures
studied in the literature are used to validate the effectiveness of the
algorithms. The results from comparative studies of the PSO–SA against
other optimization algorithms are reported to show the solution quality of
the proposed PSO−SA algorithms. The advantages and drawbacks of the
two PSO–SA algorithms are also discussed in this report.
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Wen, Yen-Ti, and 溫彥迪. "Using a Hybrid Genetic Algorithm for Placement Machine Scheduling Optimization." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/84022593471411790875.
Full textAbstract:
碩士<br>國立勤益科技大學<br>資訊工程系<br>103<br>Today, surface mount technology (SMT) is a core technology of the printed circuit board (PCB) assembly manufacturing process. For the industry, to improve the rate of the printed circuit board assembly time as the most important issue, but bottlenecks often occur in scheduler of PCB assembly. Through visits the industry and explore the related research, can know when most scholars describe the problem of placement machine optimization, they usually simplify the problem or ignore the restrictions on the placement machine hardware.
The objective of this paper was to solve the often ignored placement machine hardware restrictions in most studies, and minimized the printed circuit board assembly time. The EVEST EM-780 placement machine will be used in this experiment and focus on nozzle setup problem and pick and place sequence problem. We proposed a hybrid genetic algorithm with consider the component height, picking restrictions, pick simultaneously restrictions and placing restrictions, and we want to provide a reference for the industry in optimizing the schedule of PCB assembly.
This study proposes a hybrid genetic algorithm (HGA), the solution process is divided into two stages. The first stage would use genetic algorithms to generate an ANC sequence with the least number of pickups, and the ANC sequence as the solution of nozzle setup problem, and it would consider the component height, picking restrictions, pick simultaneously restrictions. The second stage would consider placing restrictions and the ANC sequences of first stage and would use nearest neighbor search (NNS), 2-opt and genetic algorithms, to find the pick and place sequences of least time costs of PCB assembly.
Part of the experiment will use the real PCB data that provided by EVEST EM-780 as the experimental examples. We compare our HGA with previous method and a conventional heuristic algorithm of factory engineers. The experimental results show that the proposed HGA has better performance than others.
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Liao, Jou-Chun, and 廖柔郡. "Development of a Noval Hybrid Optimization Algorithm and it’s Application." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/47147385357983660395.
Full textAbstract:
碩士<br>逢甲大學<br>航太與系統工程學系<br>101<br>The study aims at introducing a novel hybrid optimization algorithm (HOA), incorporating three different types of optimization methods, namely, genetic algorithm (GA), artificial neural network (ANN) and mathematical programming (MP), for real complex engineering design problems. The underlying idea of the proposed HOA is to take advantage of the superior features of these three different optimization algorithms while easing their drawbacks, such as, the lack of an effective termination criterion in GA. In the proposed HOA, the GA is responsible for not only evolving the population toward better fitness value but also, based on the newly-evolved populations or feasible design points at each GA generation, for continuously updating the proposed ANN mathematical model for better approximation of the objective and constraint functions. The ANN technique here is used to construct the approximate macro mathematical model or neural network model of the desired objective and constraint function. In the ANN evolution using backpropagation neural network (BPN) algorithm, the feasible design points obtained from each GA generation are considered as example pairs for training and testing the ANN model. The training would continue until the root mean square (RMS) error between the network&;#39;s output and the target value over all the example pairs is minimized. For each or every few GA generations, the newly-updated neural network models, representing the approximate objective and constraint functions, are further used to construct the optimization sub-problem. The solution of the optimization sub-problem is sought through a mathematical programming model using generalized reduced gradient (GRG) algorithm. As the optimization proceeds, a sequence of approximate solutions associated with the continuously-updated ANN models is derived. The iterative process continues until the convergence of the approximate solutions is attained. To deal with the multi-criteria and constrained optimization problems, composite objective formulation (also called weighting method) and exterior penalty method (EPM) are employed in the present HOA, respectively. Besides, several different hybrid design procedures and mutli-criteria design models are also proposed.
To determine the effectiveness of the proposed algorithm, several nonlinear programming test problems are used, in which the calculated results are compared with those of a GA and an MP algorithm, and also with the literature data. At last, the applicability of the proposed HOA is demonstrated through design optimization of a real complex engineering design problem, i.e., the design optimization of the process-induced thermal-mechanical behaviors of an anisotropic conductive film (ACF)-based ultra-thin chip on film (UTCOF) interconnect technology during bonding process. This is a multi-criteria optimization problem, in which the design objective is to seek minimization of the process-induced warpage of the silicon chip and the peeling stress at the ACF/chip interface and maximization of the contact stress at the ACF joints. Results show that the proposed HOA can be applicable for not only the ill-posed but also constrained and multi-criteria optimization problems. Furthermore, the developed algorithm can provide good optimal solutions with much less computational effort, as compared to the GA and MP method, where a larger scale of design problems would yield a more significant improvement in the computational efficiency.
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Chang, Shu-Chuan, and 張淑娟. "A Hybrid Optimization Algorithm for Two-dimensional Rectangle Packing Problems." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/cfy79d.
Full textAbstract:
博士<br>國立臺北科技大學<br>管理學院管理博士班<br>106<br>Two-dimensional rectangle packing problems (2DRPPs) are encountered in cloth, wood and paper industries when the machines cut raw materials to produce products. They arise when determining the best arrangement of a given set of rectangles inside a large rectangular object without overlapping each other. The best arrangement must meet the constraints and the objective function of the problem. The general objective of 2DRPPs is to minimize the wasted raw material or, in other words, to maximize the raw material utilization. An effective packing plan can reduce manufacturing costs by improving the utilization of raw materials, and thus, increase the firm’s competitiveness on the market, benefiting all manufacturers.
The approaches to solving 2DRPPs are classified into two major categories: deterministic and heuristic approaches. Deterministic approaches solve problems through deterministic optimization techniques to reach globally optimal solutions. However, the computational complexity significantly increases as the problem becomes larger. Heuristic approaches, in comparison, find feasible solutions efficiently, but the quality of these solutions cannot be guaranteed.
This research aims to develop a hybrid optimization algorithm through integrating heuristic and deterministic approaches to solving 2DRPPs with a near-optimal solution in an acceptable amount of computational time. The proposed approach combines a genetic algorithm (GA), a rectangle allocation strategy and a procedure based on the deterministic optimization model. The GA and the rectangle allocation strategy are utilized first to find a feasible optimal solution, then the deterministic approach is applied later for possible improvements to the solution. The hybrid optimization algorithm is further adopted to solve the berth allocation problem. Several experiment results from numerical examples indicate that the proposed approach provides feasible optimal solutions efficiently. This approach is applicable for solving practical problems.