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Seacat, Russell Holland III. "Parallelization of the simulated annealing algorithm." Diss., The University of Arizona, 1993. http://hdl.handle.net/10150/186551.
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Nuclear medicine imaging involves the introduction of a radiopharmaceutical into the body and the subsequent detection of the radiation emanating from the organ at which the procedure was directed. The data set resulting from such a procedure is generally very underdetermined, due to the dimensions of the imaging apparatus, and underconstrained due to the noise in the imaging process. A means by which more information can be obtained is through a form of imaging utilizing code-apertures. Although increasing the amount of information collected, coded-aperture imaging results in a multiplexing of the data. Demultiplexing the data requires a reconstruction process not required in conventional nuclear medicine imaging. The reconstruction process requires the optimization of an estimate to the object to be reconstructed. This optimization is done through the minimization of an energy functional. The minimization of such energy functionals requires the optimization of several parameters. Solution of this type problem is difficult because there are far too many degrees of freedom to permit an exhaustive search for an optimum, and in many cases no algorithms are known which will determine the exact optimum with significantly less work than exhaustive search. Instead, heuristic algorithms, such as the simulated annealing algorithm, have been employed and have proven effective in minimizing such energy functionals. Unfortunately, the simulated annealing algorithm, as characteristic of Monte Carlo algorithms, is very computer intensive; in fact, it is so intensive that insufficient computational power is often the chief hindrance to investigation of the algorithm. The simulated annealing algorithm, however, is amenable to parallel processing. The goal of the research in this dissertation is to investigate the parameters involved in implementing the simulated annealing algorithm in parallel; however, the form of the simulated annealing algorithm implemented here requires no annealing because the energy functionals investigated are quadratic in form. The parameters related to the parallelization of the simulated annealing algorithm include the decomposition of the reconstruction space among the processors, the formulation of the problem at the estimate level with the smallest task being a single perturbation trial evaluated on a local basis, and the communications required to keep all the processors as current as possible with changes made simultaneously to the estimate. Three objects, varying in size, shape and detail, are reconstructed utilizing the TRIMM parallel processor.
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Wade, A. S. C. "Developments of the simulated annealing algorithm." Thesis, University of East Anglia, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.300076.
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Norgren, Eric, and Johan Jonasson. "Investigating a Genetic Algorithm-Simulated Annealing Hybrid Applied to University Course Timetabling Problem : A Comparative Study Between Simulated Annealing Initialized with Genetic Algorithm, Genetic Algorithm and Simulated Annealing." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186364.
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Every semester universities around the world have to create new schedules. This task can be very complex considering that a number of constraints has to be taken into account, e.g. there should not exist any timetable clashes for students and a room cannot be double-booked. This can be very hard and time-consuming for a human to do by hand, which is why methods to automate this problem, the University Course Timetabling Problem, has been researched for many years. This report investigates the performance of a hybrid consisting of Genetic Algorithm and Simulated Annealing when solving the University Course Timetabling Problem. An implementation by Yamazaki & Pertoft (2014) was used for the Genetic Algorithm. Simulated Annealing used the Genetic Algorithm as base for its implementation. The hybrid runs the Genetic Algorithm until some breakpoint, takes the best timetable and uses it as an initial solution for the Simulated Annealing. Our results show that our implementation of Simulated Annealing performs better than the hybrid and magnitudes better than the Genetic Algorithm. We believe one reason for this is that the dataset used was too simple, the Genetic Algorithm might scale better as the complexity of the dataset increases.
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Araujo, Haroldo Alexandre de. "Algoritmo Simulated Annealing." Florianópolis, SC, 2001. http://repositorio.ufsc.br/xmlui/handle/123456789/80386.
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Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Ciência da Computação.Made available in DSpace on 2012-10-18T13:35:55Z (GMT). No. of bitstreams: 1 225675.pdf: 796704 bytes, checksum: 892abc8468e4e7c6715b6c3f2de50e51 (MD5)
A busca por soluções de problemas por meio do computador é o tema central da ciência da computação, relevante para grande parte da ciência e de suas aplicações tecnológicas. Essa busca, certamente, vai na direção de algoritmos eficientes e exatos mas que nem sempre boas soluções podem ser encontradas para muitos problemas de ordem prática, principalmente, no que diz respeito a tempo de execução. Existem problemas, dentre estes, os de otimização combinatorial que apresentam uma peculiaridade com relação aos outros, que é a grande dificuldade de se obter soluções exatas num tempo computacional aceitável. Atualmente, as novas técnicas, especialmente as metaheurísticas, tais como: Tabu Search, Simulated Annealing, Algoritmos Genéticos e Redes Neurais, vêm conseguindo sucesso na solução de problemas de otimização combinatorial, que mesmo não apresentando soluções exatas têm mostrado bastante eficiência com suas soluções aproximadas. Este trabalho propõe um novo método baseado no algoritmo Simulated Annealing (SA) através de mudanças bruscas nos valores da temperatura que são retiradas de múltiplas faixas, ao contrário do SA básico, onde esses valores são obtidos de uma faixa única, ou seja, num SA básico, os valores assumidos pela temperatura saem de um intervalo, partindo de um valor inicial, e vão diminuindo até um valor final. Tais mudanças bruscas acontecem exatamente no momento da mudança de faixa, pois o valor da temperatura que no final de uma faixa é pequeno, assume um valor correspondente a temperatura inicial da faixa seguinte, normalmente, bem maior. Posto a prova, com instâncias euclidianas do Problema Caixeiro Viajante, que é um problema de otimização combinatorial de difícil solução, o método apresenta resultados bastante satisfatórios quando comparado com o SA básico.
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Moins, Stephane. "Implementation of a Simulated Annealing algorithm for Matlab." Thesis, Linköping University, Department of Electrical Engineering, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1344.
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In this report we describe an adaptive simulated annealing method for sizing the devices in analog circuits. The motivation for use an adaptive simulated annealing method for analog circuit design are to increase the efficiency of the design circuit. To demonstrate the functionality and the performance of the approach, an operational transconductance amplifier is simulated. The circuit is modeled with symbolic equations that are derived automatically by a simulator.
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Sohn, Eugene. "Simulated annealing algorithm for customer-centric location routing problem." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/117923.
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Thesis: M. Eng. in Supply Chain Management, Massachusetts Institute of Technology, Supply Chain Management Program, 2018.Cataloged from PDF version of thesis.
Includes bibliographical references (pages 36-38).
In today's world, the e-commerce market is growing rapidly and becoming more competitive. While many players in the industry are attempting to get their share of pie, consumers are demanding faster deliveries and free shipping. This market growth and change in consumer behavior provides an exciting opportunity for companies to compete. In order to meet the new consumer demand, companies need to find better ways to deliver faster. Faster delivery times can be achieved by using an optimization model to plan delivery network and operations. Typically, this optimization model has been based on minimizing cost. However, in the current market, lowest cost is not necessarily the best driver of sales as the consumer culture enters an era of instant gratification. We argue that minimizing customer waiting time will bring better performance and win over market share by providing the quickest delivery service that is expected by the majority of consumers. We propose solving the location routing problem (LRP) aiming at minimizing customer waiting time with capacitated depots and vehicles. We take two approaches to solve this problem: mathematical model and heuristic algorithm. The mathematical model obtains the optimal solution, but it has a limitation on the size of the problem due to the NP-hardness of the LRP. Therefore, we introduce three different variations of Simulated Annealing (SA) algorithm to solve the Capacitated Latency Location Routing Problem (CLLRP). According to the comparison results on a popular benchmark test, one of the designed SAs, the Iterative Simulated Annealing algorithm, consistently provides the best combination of performance and computation time compared to the other two SAs. Therefore, this specific algorithm is further compared to the mathematical model on some problem instances. The comparison results demonstrate that the proposed algorithm performs competitively with the algorithms in the literature and the mathematical model.
by Eugene Sohn.
M. Eng. in Supply Chain Management
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Fleischer, Mark Alan. "Assessing the performance of the simulated annealing algorithm using information theory." Case Western Reserve University School of Graduate Studies / OhioLINK, 1994. http://rave.ohiolink.edu/etdc/view?acc_num=case1057677595.
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Gelfand, Saul B. (Saul Brian). "Analysis of simulated annealing type algorithms." Thesis, Massachusetts Institute of Technology, 1987. http://hdl.handle.net/1721.1/14935.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1987.MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING
Bibliography: leaves 101-103.
by Saul B. Gelfand.
Ph.D.
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Kovàcs, Akos. "Solving the Vehicle Routing Problem with Genetic ALgorithm and Simulated Annealing." Thesis, Högskolan Dalarna, Datateknik, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:du-3306.
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This Thesis Work will concentrate on a very interesting problem, the Vehicle Routing Problem (VRP). In this problem, customers or cities have to be visited and packages have to be transported to each of them, starting from a basis point on the map. The goal is to solve the transportation problem, to be able to deliver the packages-on time for the customers,-enough package for each Customer,-using the available resources- and – of course - to be so effective as it is possible.Although this problem seems to be very easy to solve with a small number of cities or customers, it is not. In this problem the algorithm have to face with several constraints, for example opening hours, package delivery times, truck capacities, etc. This makes this problem a so called Multi Constraint Optimization Problem (MCOP). What’s more, this problem is intractable with current amount of computational power which is available for most of us. As the number of customers grow, the calculations to be done grows exponential fast, because all constraints have to be solved for each customers and it should not be forgotten that the goal is to find a solution, what is best enough, before the time for the calculation is up. This problem is introduced in the first chapter: form its basics, the Traveling Salesman Problem, using some theoretical and mathematical background it is shown, why is it so hard to optimize this problem, and although it is so hard, and there is no best algorithm known for huge number of customers, why is it a worth to deal with it. Just think about a huge transportation company with ten thousands of trucks, millions of customers: how much money could be saved if we would know the optimal path for all our packages.Although there is no best algorithm is known for this kind of optimization problems, we are trying to give an acceptable solution for it in the second and third chapter, where two algorithms are described: the Genetic Algorithm and the Simulated Annealing. Both of them are based on obtaining the processes of nature and material science. These algorithms will hardly ever be able to find the best solution for the problem, but they are able to give a very good solution in special cases within acceptable calculation time.In these chapters (2nd and 3rd) the Genetic Algorithm and Simulated Annealing is described in details, from their basis in the “real world” through their terminology and finally the basic implementation of them. The work will put a stress on the limits of these algorithms, their advantages and disadvantages, and also the comparison of them to each other.Finally, after all of these theories are shown, a simulation will be executed on an artificial environment of the VRP, with both Simulated Annealing and Genetic Algorithm. They will both solve the same problem in the same environment and are going to be compared to each other. The environment and the implementation are also described here, so as the test results obtained.Finally the possible improvements of these algorithms are discussed, and the work will try to answer the “big” question, “Which algorithm is better?”, if this question even exists.
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Batts, William Merle. "Modeling of a hardware VLSI placement system : accelerating the simulated annealing algorithm /." Link to online version, 2005. https://ritdml.rit.edu/dspace/handle/1850/1015.
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Molvalioglu, Orcun. "Interacting-particle algorithm and meta-control of temperature parameter /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/10687.
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Abduelmola, Abduelghani I. "Modeling of cellular manufacturing systems with productivity consideration, a simulated annealing algorithm." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0014/NQ52416.pdf.
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Chen, Liang. "Motif Selection Using Simulated Annealing Algorithm with Application to Identify Regulatory Elements." Ohio University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1531343206505855.
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Kuo, Hsien-Chih. "Applying network flow and simulated annealing in job shop rescheduling due to machine breakdowns." Ohio : Ohio University, 1996. http://www.ohiolink.edu/etd/view.cgi?ohiou1178224749.
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Zhang, Xiaodong. "A Local Improvement Algorithm for Multiple Sequence Alignment." Ohio University / OhioLINK, 2003. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1049485762.
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Dittmer, Jonathan Keith. "The stochastic inversion of magnetics and resistivity data using the simulated annealing algorithm." Thesis, University of York, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.359226.
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Tran, Mua Dinh Lam. "Experiences with a synchronous parallel simulated annealing algorithm for the traveling salesman problem." Thesis, Massachusetts Institute of Technology, 1989. http://hdl.handle.net/1721.1/42980.
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Sweeney, James P. "Dual Constraint Problem Optimization Using A Natural Approach: Genetic Algorithm and Simulated Annealing." UNF Digital Commons, 2007. http://digitalcommons.unf.edu/etd/283.
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Constraint optimization problems with multiple constraints and a large solution domain are NP hard and span almost all industries in a variety of applications. One such application is the optimization of resource scheduling in a "pay per use" grid environment. Charging for these resources based on demand is often referred to as Utility Computing, where resource providers lease computing power with varying costs based on processing speed. Consumers using this resource have time and cost constraints associated with each job they submit. Determining the optimal way to divide the job among the available resources with regard to the time and cost constraints is tasked to the Grid Resource Broker (GRB). The GRB must use an optimization algorithm that returns an accurate result in a timely mam1er. The Genetic Algorithm and the Simulated Annealing algorithm can both be used to achieve this goal, although Simulated Annealing outperforms the Genetic Algorithm for use by the GRB. Determining optimal values for the variables used in each algorithm is often achieved through trial and error, and success depends upon the solution domain of the problem. Although this work outlines a specific grid resource allocation application, the results can be applied to any optimization problem based on dual constraints.
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Brugaletta, Luca. "Randomized configuration for Algorithm Selector SUNNY." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/17573/.
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In questa tesi abbiamo analizzato i comportamenti dell'algoritmo Sunny andando a modificare il metodo di scelta delle feature, principalmente passando da un approccio sequenziale ad uno casuale.
Abbiamo implementato e confrontato 3 tecniche oltre a quella di partenza:
-randk: sfrutta un approccio puramente casuale per il calcolo delle n feature e della k.
-simann: sfrutta la tecnica di ottimizzazione di simulated annealing per calcolare le n feature e il valore di k.
-simann-mod: come simann, ma utilizza parametri diversi per il calcolo.
All'interno di questa tesi troviamo i risultati dell'esperimento e i vantaggi che si possono avere nell'utilizzo di una tecnica che non visita tutte le possibili soluzioni, ma solamente un numero ridotto di esse.
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Nwana, Vincent Lebga. "Parallel algorithms for solving mixed integer linear programs." Thesis, Brunel University, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.368540.
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Song, Yinglei. "Grammatical Study of Ribonucleic Acids Pseudo-Knot Structures: A Simulated Annealing Approach." Ohio : Ohio University, 2003. http://www.ohiolink.edu/etd/view.cgi?ohiou1071064106.
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Ardö, Edvin, and Johan Lindholm. "A comparative study between a genetic algorithm and a simulated annealing algorithm for solving the order batching problem." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254929.
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Optimizing warehouse automation requires finding efficient routes for pickingup items. Dividing the orders into batches is a realistic requirement for warehouses to have. This problem, known as the order batching problem, is an NP-hard problem. This thesis implements and compares two meta-heuristics to the order batching problem, simulated annealing (SA) and a genetic algorithm(GA). SA was found to perform equal to or better than GA on all occasions in terms of minimizing traveling distance. The algorithms were tested on 6 different warehouses with various layouts. The algorithms performed similarly on the smallest problem size, but in the largest problem size SA managed to find 17.1 % shorter solutions than GA. SA tended to find shorter solutions in a smaller amount of time as well.Optimering av automatiserade varuhuslager kräver att effektiva rutter hittas för att hämta upp varor. Att dela upp ordrarna i grupper är ett realistiskt krav som lager kan ha. Detta problem, som kallas för order batching-problemet, är ett NP-svårt problem. Detta kandidatarbete jämför två implementationer av meta-heuristiker till order batching-problemet, simulated annealing (SA) och genetic algorithm (GA). SA visade sig vara lika bra eller bättre än GA vid alla tillfällen då målet är att minimera den totala färdsträckan. Algoritmen testades på 6 olika varuhus som hade olika designer. Algoritmerna kom fram till liknande lösningar för de minsta varuhusen, men i det största varuhuset lyckades SA hitta en lösning som var 17.1 % bättre än GA. SA tenderade även att hitta kortare lösningar givet mindre tid.
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Weldon, Ruth. "Using Simulated Annealing for Robustness in Coevolutionary Genetic Algorithms." NSUWorks, 2014. http://nsuworks.nova.edu/gscis_etd/334.
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Simulated annealing is a useful heuristic for finding good solutions for difficult combinatorial optimization problems. In some engineering applications the quality of a solution is based upon how tolerant the solution is to changes in the environment. The concept of simulated annealing is based upon the metallurgical process of annealing where a material is tempered by heating and cooling.
Genetic algorithms have been used to evolve solutions to complex problems by imitating the biological process of evolution using crossover and mutation to modify the candidate solutions. In coevolution a candidate solution is composed of multiple species each of which provides a portion of the candidate solution. Those individuals of a species that, in collaboration with the individuals from the other species, are evaluated as providing the most fit solution are the preferred individuals of a species.
This work investigated whether robustness, defined as the ability of a solution to tolerate changes to the problem environment, could be improved by defining a neighborhood of fitness functions that are centered in the neighborhood of the nominal objective function. Simulated annealing was used to manage the subsequent narrowing of the neighborhood of fitness functions. Two robustness measures were developed that used samples from the neighborhood of objective functions; one employed the minimum fitness value, and the other employed the average fitness value. Coevolutionary genetic algorithms were used to generate candidate solutions employing the robustness measures.
This study used three benchmark functions to evaluate the effects of the robustness measures. The results indicated that the robustness measures could produce solutions that were robust and, often, globally optimal for benchmark functions employed in the testing. Future work includes applying this framework to a broader class of optimization problems, investigating new neighborhood strategies, and devising new robustness measures.
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Salman, Alzahraa, and Rouwayd Hanna. "A Comparative Study between Genetic Algorithm, Simulated Annealing and a Hybrid Algorithm for solving a University Course Timetabling Problem." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229432.
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Every year, universities are faced with the problem of having to schedule events to various resources such as lecturers, classrooms and time slots while considering different constraints. The University Course Timetabling Problem is a NP-complete combinatorial optimization problem that, if solved manually, requires great investment in time and money. Thus finding an algorithm that automates this process would prove beneficial for society. The aim of this thesis is to compare the performance of a Genetic Algorithm-Simulated Annealing hybrid implementation with the performance of each of the algorithms individually for solving the University Course Timetabling Problem. The data sets used were inspired by the Royal Institute of Technology in Stockholm. The results showed that Simulated Annealing performed better than the other two algorithms, with respect to time consumption. However, the hybrid algorithm showed great promise of actually producing a feasible solution before terminating as the complexity of the problem increased, for example in the biggest data set tested.Varje år står universitetet inför problemet med att planera händelser till olika resurser, såsom föreläsare, klassrum och tidsluckor, med hänsyn till flertal fördefinierade villkor. Universitetsschemaläggningsproblemet är ett NP-fullständigt kombinatoriskt optimeringsproblem som kräver mycket tid och pengar om det löses manuellt. Att hitta en algoritm som automatiserar denna process skulle därför vara till nytta för samhället. Syftet med denna avhandling är att jämföra prestandan för en Genetisk Algoritm-Simulerad Glödning hybrid implementering med prestandan för var och en av algoritmerna individuellt för att lösa Universitetsschemaläggningsproblemet. Datamängderna som används är inspirerade av Kungliga Tekniska Högskolan i Stockholm. Resultaten visade att Simulerad Glödning presterade bättre än de andra två algoritmerna, med hänsyn till tidskonsumtion. Hybrid algoritmen visade dock ett stort potential att faktiskt ta fram en acceptabel lösning, innan den terminerar, när komplexiteten av datat ökade, till exempel i det största datasetet som testades.
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Haeser, Gabriel. "Algoritmo duas fases em otimização global." [s.n.], 1996. http://repositorio.unicamp.br/jspui/handle/REPOSIP/305937.
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Orientador: Marcia A. Gomes RuggieroDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Computação Cientifica
Made available in DSpace on 2018-08-05T23:37:23Z (GMT). No. of bitstreams: 1 Haeser_Gabriel_M.pdf: 906525 bytes, checksum: ea7e3eb42abe6b8b451f99c4c63a3da4 (MD5) Previous issue date: 1996
Resumo: Neste trabalho estudamos a teoria de algumas heurísticas para otimização global, e também a generalização do algoritmo genético de Aarts, Eiben e van Hee. Propomos um algoritmo para otimização global de problemas canalizados e diferenciáveis utilizando simulated annealing e o solver local GENCAN. Experimentos numéricos com o problema OVO ( Order- Value Optimization) são apresentados, e também com 28 problemas clássicos da literatura. Para problemas de otimização com restrições, apontamos idéias de como utilizar solvers locais e heurísticas globais em busca de bons algoritmos para otimização global, e propomos um algoritmo baseado em simulated annealing com solver local ALGENCAN
Abstract: In this work we study the theory behind some classical heuristics for global optimization, and a generalization of genetic algorithms from Aarts, Eiben and van Hee. We propose an algorithm for global optimization of box-constrained differentiable problems, using simulated annealing and the local solver GENCAN. Numerical experiments are presented for the OVO problem (Order-Value Optimization) and 28 classical problems. For general nonlinear programming problems, we mention some ideas of how to use local solvers and global heuristics towards good algorithms for global optimization, we also propose an algorithm based on simulated annealing with local solver ALGENCAN
Mestrado
Otimização
Mestre em Matemática Aplicada
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Herrera, Claudia Natalia Lara. "Algoritmo de tomografia por impedância elétrica baseado em Simulated Annealing." Universidade de São Paulo, 2007. http://www.teses.usp.br/teses/disponiveis/3/3152/tde-28012008-172456/.
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A Tomografia por Impedância Elétrica (TIE) é uma técnica não invasiva usada para produzir imagens que representam a distribuição de resistividade, ou condutividade, de uma seção transversal dentro de um domínio, por vezes o tórax humano, a partir do conhecimento de medidas elétricas feitas através de eletrodos distribuídos na sua fronteira. Correntes injetam-se e medem-se voltagens ou vice-versa. Distribuição de variação de resistividade ou distribuição de valor absoluto de resistividade podem ser estimadas, gerando algoritmos ditos de diferenças ou absolutos. O presente trabalho avalia o desempenho de um algoritmo probabilístico baseado no método Simulated Annealing (SA) para obter distribuições absolutas de resistividade em duas dimensões (2D). O SA difere dos métodos tradicionais de busca, tem a capacidade de escapar de mínimos locais graças ao emprego do critério de Metropolis para a aceitação dos novos pontos no espaço de busca e não precisa da avaliação de derivadas da função objetivo. O algoritmo desenvolvido soluciona o problema inverso da TIE ao resolver iterativamente um problema direto, utilizando distribuições de resistividade obtidas por sorteio aleatório. O sorteio é realizado pelo algoritmo de Metropolis. Na ausência de regularizações, assume-se que a imagem sorteada que minimiza a diferença entre as voltagens medidas na fronteira do domínio e as calculadas é a que mais se aproxima da distribuição de resistividade real. Neste sentido, a imagem final maximiza a verossemelhança. Este trabalho contribui com o desenvolvimento de algoritmos para estimação de imagem aplicados para monitorar a ventilação mecânica dos pulmões. Uma vez que se pretende resolver um problema inverso, não-linear e mal-posto é necessário introduzir informação a priori, na forma de restrições do espaço solução ou de regularizações. São realizados ensaios com dados simulados por meio de um fantoma numérico, dados de bancada experimental e dados provenientes de um tórax humano. Os resultados mostram que a localização, o tamanho e a resistividade do objeto estão dentro da precisão da TIE obtida por métodos clássicos, mas o esforço computacional é grande. Verificam-se, assim, as vantagens e a viabilidade do algoritmo proposto.The Electrical Impedance Tomography (EIT) is a non-invasive technique used to produce images that represent the cross-sectional electrical resistivity distribution, or conductivity, within a domain, for instance the human thorax, from electrical measurements made through electrodes distributed on its boundary. Currents are injected and voltages measured, or vice-versa. Distributions of resistivity variations or distributions of absolute resistivity can be estimated, producing difference or absolute algorithms. The present work develops and evaluates the performance of a probabilistic algorithm based on the Simulated Annealing method (SA) to obtain absolute resistivity distributions in two dimensions (2D). The SA differs from the traditional search methods, no evaluation of objective function derivatives is required and it is possible to escape from local minima through the use of the Metropolis criterion for acceptance of new points in the search space. The developed algorithm solves the inverse problem of EIT by solving iteratively a direct problem, using random resistivity distributions. The random search is accomplished by the Metropolis algorithm. In the absence of regularizations, it is assumed that the resistivity distribution, an image, that minimizes the difference between the measured electrical potentials on the boundary and computed electrical potentials is the closest to the real resistivity distribution. In this sense, the algorithm maximizes the likelihood. This work contributes to the development of image estimation algorithms applied to lung monitoring, for instance, during mechanical ventilation. To solve this non-linear ill-posed inverse problem it is necessary to introduce prior information in the form of restrictions of the solution space or regularization techniques. The tests are carried out using simulated data obtained from a numerical phantom, an experimental phantom and human thorax data. The results show that the localization of an object, the size of an object and the resistivity of an object are within the accuracy of EIT obtained by classical methods, but the computational effort is large. The advantages and feasibility of the proposed algorithm were investigated.
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Nolle, Lars. "Application of computation intelligence to optimisation problems in the hot rolling of wide steel strip." Thesis, Open University, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.301882.
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Jahangiri, Arash. "Modeling and Assessing Crossing Elimination as a Strategy to Reduce Evacuee Travel Time." Thesis, Virginia Tech, 2012. http://hdl.handle.net/10919/76873.
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During evacuations, emergency managers and departments of transportation seek to facilitate the movement of citizens out of impacted or threatened areas. One strategy they may consider is crossing elimination, which prohibits certain movements at intersections, that may be permissible under normal operating conditions. A few previous studies examined this strategy in conjunction with contra-flow operations, but fewer have considered crossing elimination by itself. This study helps fill the existing gap in knowledge of the individual effects of crossing elimination.
A bi-level model that iterates between optimization and simulation is developed to determine the optimal configuration of intersection movements from a set of pre-specified possible configurations for intersections in a given area. At the upper level, evacuees' travel time is minimized and at the lower level, traffic is assigned to the network with the traffic assignment-simulation software DynusT. The overall model is solved with a simulated annealing heuristic and applied to a real case study to assess the impact of crossing elimination.
Three scenarios are developed and examined using the solution method proposed in this research. These scenarios are developed using combinations of two elements: (1) Evacuee destination distributions, and (2) Evacuee departure time distributions. Results showed about 3-5 percent improvement in total evacuee travel time can be achieved in these scenarios. Availability of through movements at intersections and existing merging points in movement configurations are the two factors influencing the selection of movement configurations.Master of Science
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Fredrikson, Rasmus, and Jonas Dahl. "A comparative study between a simulated annealing and a genetic algorithm for solving a university timetabling problem." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-187158.
Full textAbstract:
The university timetabling problem is an NP-complete problem which schools all over the world face every semester. The aim of the problem is to schedule sets of events such as lectures and seminars into certain time slots without violating numerous specified constraints. This study aimed to automate this process with the help of simulated annealing and compare the results with a genetic algorithm. The input data sets were inspired by the Royal Institute of Technology in Stockholm. The results showed a great run time difference between the two algorithms where the simulated annealing performed much better. They also showed that even though the simulated annealing algorithm was better during all stages, the genetic algorithm had a much better performance in early stages than it had in latter. This led to the conclusion that a more optimized, hybrid algorithm could be created from the two algorithms provided that the genetic algorithm could benefit from the improvements suggested in previous research.Universitetsschemaläggningsproblemet är ett NP-fullständigt problem som skolor över hela världen måste hantera innan varje termin. Syftet med problemet är att schemalägga händelser, såsom föreläsningar och seminarier, utan att bryta flertalet fördefinierade villkor. Denna studie hade som mål att automatisera denna process med hjälp av algoritmkonstuktionsmetoden simulerad glödgning och sedan jämföra resultatet med en genetisk algoritm. De datamängder som användes är inspirerade av den verkliga situationen på KTH. Resultaten visar stora tidsmässiga skillnader där algoritmen baserad på simulerad glödgning går snabbare. De visar dock också att den genetiska algoritmen har en bättre prestanda i tidigare stadier än i senare. Detta ledde till slutsatsen att en mer optimerad hybridalgoritm kan skapas av de två algoritmerna, förutsatt att den genetiska algoritmen kan dra nytta av förbättringar som föreslagits i tidigare forskning.
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"Convergence of the simulated annealing algorithm." Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, 1988. http://hdl.handle.net/1721.1/3084.
Full text
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Huang, Chen-Fu, and 黃晨富. "SoC Test Scheduling using Simulated Annealing Algorithm." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/76519178111171109290.
Full textAbstract:
碩士國立臺北科技大學
電腦通訊與控制研究所
92
As the semiconductor technology has made great progress, System-on-a-Chip (SoC) has become the kernel technologies for integrating computer, consumer, and communication. Test scheduling is an important problem in SoC test automation. In this paper, we propose a SoC test scheduling method which is based on simulated annealing algorithm. A data structure, called a sequence pair, is used to represent the placement of rectangles, because the scheduling is formulated as a two-dimensional bin packing problem. We also consider power consumption restriction and reduce idle time in SoC test scheduling. Experiments are conducted on ITC’02 benchmarks. Experiment results show our proposed method is efficiently and the test time is shorter than earlier methods about 3% ~ 24%.
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Chen, Wei-Chang, and 陳瑋昶. "The Improvement of Parallel Simulated Annealing Algorithm." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/13983867760770246022.
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碩士國立清華大學
工業工程與工程管理學系
91
Simulated annealing is inspired by the physical process of heating and cooling process to obtain a strong crystalline structure. It is a heuristic algorithm that can be used to solve discrete optimization problems. The concepts of parallel computing were applied to speed up computation. It allows us obtain solutions in a shorter time. Multiple trials is the basic concept of many parallel simulated annealing. It is to perform multiple trials at the same time to search acceptable solutions. The division simulated annealing with no communication and The clustering simulated annealing are two common types of parallel simulated annealing methods. The division simulated annealing with no communication makes each processor generate its own adjacent solution from its own current solution and compute the acceptance probability for a adjacent solution. Each of the processors searches solutions without communicating each other. Clustering simulated annealing makes each processor generate its own adjacent solution from a common current solution and compute the acceptance probability for each adjacent solution. Then, it decides which adjacent solution should be used to replace current solution according to a pre-specified rule. Dynamic parallel simulated annealing (DPSA) combined independent simulated annealing with clustering simulated annealing. At high temperature, DPSA makes each processor search in it own sequence independently; while at low temperature, DPSA merges processors into clusters that performs a common search sequence. The focus of this research is how and when we should combine processors of independent search sequences into a cluster. There are two dynamic parallel simulated annealing algorithm used in this research. Both of them have better search result than independent simulated annealing.
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Wen, Tien-Cheng, and 溫添成. "Parallelization of Simulated Annealing and Genetic Algorithm." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/64201275681807443131.
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Liu, Jia-Je, and 劉嘉哲. "Camera Calibration Using an Orthogonal Simulated Annealing Algorithm." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/m7jhu8.
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碩士逢甲大學
資訊工程所
91
Camera calibration plays an important role in many machine vision and photogrammetric applications. A genetic algorithm (GA) based approach to camera calibration has been shown to be efficient in terms of convergence, accuracy, and robustness, compared with the famous non-evolutionary method, Tsai''s procedure. In this thesis, a novel approach to high-accuracy camera calibration using an orthogonal simulated annealing algorithm OSA is presented. OSA is an efficient optimization algorithm composed of simulated annealing algorithm and orthogonal experimental design. OSA can across local optima and find a nearly global optimal solution. The same test experiments are conducted to ensure fair comparison with the GA approach. Experimental results reveal that the proposed OSA-based approach is more efficient in terms of convergence, accuracy, and robustness.
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WU, JUN-KUAN, and 吳俊寬. "Machine cell formation: using the simulated annealing algorithm." Thesis, 1991. http://ndltd.ncl.edu.tw/handle/80721320723723972105.
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Huang, Yu-Fang, and 黃鈺芳. "Simulated Annealing Algorithm for the Construction Facility Layout Problems." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/09700874078988241022.
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碩士朝陽科技大學
營建工程系碩士班
95
The quality of construction facility layout is closely related to the project cost, production performance and construction fluency. In fact, seeking an effective construction facility layout is a crucial issue for construction industry in order to reduce the cost and improve the productivity. In this paper, the simulated annealing algorithm whose objective function is established by the distance and the contacts frequency between the facilities is applied on the problems of construction facility layout. The thesis demonstrates that the simulated annealing algorithm is able to effectively and rapidly find the optimization solutions in the issues of construction facility layout.
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Sun, Szu_Wei, and 孫思瑋. "Design of Power Harmonic Filter by Simulated Annealing Algorithm." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/81266384298343883820.
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碩士淡江大學
電機工程學系
84
To improve the power quality, generally it is the voltage and current disto-rtion (harmonic) to install harmonic filters in industry power systems. The p-erformance and cost of filters are of the most consider concern to the users. A new formulation of the design harmnonic filter problem taking into conside-ration practical aspects of investment cost and operational constraints the n-eu formulation is a combinatiorial optimization problem with a non-differenti-able objective function. A solution methodology based on an optimization tech-nique-simmulated annealing to determine (1)the cost of filters (2)the size of filters is proposed. The proposed solution methodology can offer the optimalsize and minimum cost of the design filters. Harmonic (voltage and current distortion) in power systems shortens the lifeexpectancy of equipment and can interfere with communication lines and sensit-ive equipment. The recent growing concern for this problem results from the i-ncreasing numbers and power ratings of the highly non-linear power electronic devices used in the control of power apparatus in industrial power systems. T-he filter design has been get necessary for industrial power systems. Traditi-onally, the problem of design a harmonic filter has handled by trial and errorapproach. In the past decade, various formulations for a more systematic appr- oach to harmonic filters design has been developed. Although many of the exis-ted methods is effective to eliminate harmonic, but those only discuss for thevalue of filter elements. They did not consider whether the industry can supp-ly the specification of the design values or not. In this paper, we consider not only the above question but also the cost of the filters in order to get optimal value of a filter and to meet the practical requirement.
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廖梓淵. "Simulated Annealing and Genetic Algorithm for Automatic Velocity Picking." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/45825202688930458988.
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碩士國立交通大學
多媒體工程研究所
100
We propose a system of automatic velocity picking in seismic data using simulated annealing and genetic algorithm. Velocity picking is to pick the time-velocity pairs representing the relation of stacking velocity and time based on semblance image. Conventionally seismic velocity picking is processed by geophysical experts through looking at the semblance image and geological surveying, which is time consuming. In the semblance image, we choose peaks to become the velocity plot. This velocity plot passes through the peaks having the maximal summation of energy, and satisfies some velocity constraints. So the velocity picking can be formulated into a combinatorial optimization problem. We determine maximal peaks on semblance by the maximum filter, then use simulated annealing and genetic algorithm to find the global optimal solution in the large finite set of peak combinations. The result of genetic algorithm is better. Then we apply the obtained velocity to do the normal moveout correction and stacking. The whole process is automatic, and the velocity picking can improve the seismic interpretation as well.
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Chung, Wei-Shin, and 鍾偉信. "A Simulated Annealing Algorithm for Market Based Redistricting Problems." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/47922215372617149532.
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碩士國立臺灣大學
建築與城鄉研究所
92
In urban study, there are many redistricting problems (RP), which subdivide spaces based on various activity intensities. In the past, such problems usually set their objective functions in accordance with the Euclidian distance from certain points. However, distance is not the only consideration, in stead, market shares is probably the other factor to determine the service areas of some industries, like electronic power, telecommunications or cable TV. Therefore, a new algorithm for market based RP (MRP) is needed. In this study, to comply with the MRP which has multi-minimal solutions, the simulated annealing algorithm (SA), capable of escaping from a local minimal to seek a global one, is adopted by taking power service MRP as an illustrative case. The study area is represented as a grid system. After estimating the volumes of power consumption for every grid, the study area is partitioned into a given number of contiguously grid regions subject to the total difference of power consumptions of all the regions is minimized. This research focuses on finding appropriate initial values of some parameters of SA to improve computational efficiency while satisfied solutions can still be obtained. In the illustrative case, it concludes that: (1) SA is confirmed to be a valid algorithm for MRP; (2) A grid size representing a 2×2 KM2 area is recommended; (3) Given a tolerable error, the number of iteration times can be sufficiently set to to have credible solutions with 95% confidence level.
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王嘉鵬. "Solving the University Course Scheduling Using Simulated Annealing Algorithm." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/15084326866452672773.
Full textAbstract:
碩士育達商業技術學院
資訊管理所
92
The school timetabling problem involves both NP-complete and combinatorial optimisation problems, hence it is quite difficult while seeking for solutions. Timetabling is now manually achieved in most schools, or, a general timetable is made and then amended by operators according to teachers’ equirements. It not only consumes human resources but also earns a low satisfaction level. Therefore, purpose of this research is to improve satisfaction level of original timetable using simulated annealing algorithm. Essential parameters affecting simulated annealing algorithm are discussed as well to make it efficient, and to increase possibility discovering optimal school timetable. Concept of simulated annealing algorithm is to imitate real annealing process of metals. It is applied to solve large-scale combined problems since its unique probability hill-climb search helps to avoid occurrence of local optimum, which leads to failure of acquiring global optimum, while solving problems. Satisfaction level can be elevated due to this characteristic. Although the theory is simple, it is difficult to perform simulated annealing because a significant cooling schedule must be set according to types and dimensions of metals. An essential parameter in cooling schedule is cooling factor, which is adopted to enhance efficiency and quality while seeking solutions. There are two parts in this research. First, courses are allocated to available classrooms using simulated annealing algorithm. Teachers’ favourable period and proper course-arranging time are indices while calculating satisfaction levels. Optimal solution for satisfaction level is thus obtained via simulated annealing. Second, cooling factor is investigated. Adopting an appropriate cooling factor can lower possibility of local optimum. This further elevates probability in locating global optimum and generates a timetable deserving optimum and satisfaction.
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Fang, Chueng-Yiang, and 方崇仰. "Genetic Algorithm enhanced Simulated Annealing Method on Molecular Structure." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/28612102049280710641.
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碩士國立中山大學
物理學系研究所
88
As a result of “the X-ray Phase Problem”, traditional direct methods can’t solve the structures of the large molecules. For exploring alternate methods, Wu-Pei Su applied simulated annealing to solve the structure of the large molecules and obtained success. Adopting his concept, we wrote a program for solving the structure of the molecules by C Program Language. And for decreasing the running time of the program, we introduced the concept of genetic algorithm into simulated annealing method.
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Liu, Hsin-chun, and 劉新春. "Using Simulated Annealing Algorithm for Data Broadcasting in Mobile Environment." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/17516968347561038126.
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碩士南華大學
資訊管理學研究所
93
With the highly developed internet gradually becoming more and more widespread and the availability of computer related facilities, such as mobile phone, personal digital assistant (PDA) and notebook, there is no doubt mobile communication is becoming one of the most convenient ways for people get information in their daily life.Wireless network has become the main stream in network communities. In wireless environment, data broadcast is a way to provide a great deal of information for users. Because many customers are not able to make commercial transactions through internet in a wireless environment, and because of the lack of enough bandwidth and the overload of servers, the quality of transactions is greatly affected. In order to make users get the related information they need in a more efficient way, a well designed broadcasting system is required. It can provide an appropriate broadcasting schedule to satisfy the needs of the users, and therefore reduces the time spent on the process of data retrieving, and speeds up data accessing in a broadcasting environment. This thesis takes advantage of Simulated Annealing to find out a better data broadcast ordering to address the above issues. Experimental results show that our simulated annealing approach does efficiently reduce the access times of client uses.
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Huang, Wen-Hsing, and 黃文星. "Using Simulated Annealing Algorithm to Segment Brain Magnetic Resonance Images." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/02144898111212142381.
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碩士大葉大學
工業工程學系碩士班
91
This research investigates heuristic algorithms for segmenting human brain magnetic resonance images (MRI). The preliminary results of K-mean, fuzzy algorithm for learning vector quantization (FALVQ), learning vector quantization (LVQ), and fuzzy C-mean clustering algorithm are used as initial solutions for different simulate annealing (SA), such as classical SA (CSA), fast SA (FSA), generalized SA (GSA), adaptive SA (ASA), and Tsallis SA (TSA). These different algorithms are based on different moving rules and annealing processes. This research estimates twenty of the combination methods to locate the meningggioma and calculates the index of the effort. The experimental results show that the combination of FALVQ and ASA is better than others to segment the MRI in the human brain.
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Pao, Chuan Chen, and 陳寶娟. "A Simulated Annealing Algorithm Approach for Floorplanning with Boundary Constraints." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/49935489374225018385.
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碩士國立高雄應用科技大學
電子工程系
98
Two floorplan methodologies are studied to increase the circuit efficiency and minimize the chip size in this thesis. The first method is to use the Simulated Annealing Algorithm and B*-Tree. This method is to relocate the modules with boundary conditions to the chip edge when the number of edge modules is less than 1000. The second method is to use the perturbation method to reduce the computer time when the number of edge modules is larger than 1000. The theory and experiment results indicate that the two methods can increase the chip utility area and decrease the computer time by fixing the modules with boundary conditions to the chip edge. When the total module number is 10000, the edge module with boundary condition is 100 and dead area is 20%, the computer time can reduce from 7500 seconds to 2500 seconds by using simulated annealing algorithm and B*-Tree method. Furthermore, the experiment results also indicate that the computer time can reduce 28% when using the perturbation method. This method is significantly reducing the computer time to floor plan of module number larger than 1000.
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Cheng, Ren-Fu, and 鄭仁福. "Genetic Algorithm and Simulated Annealing Combined to Feeder Capacitor Placement." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/96878222651581659038.
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碩士國立臺北科技大學
電機工程系碩士班
89
Electric power system mainly consists of three parts , which are generation, transmission and distribution. Distribution system has the direct contact with the users ranging from crowded cities to remote countryside. The distribution system is complicated. Therefore, a good planning of distribution system will enhance the power quality and stability of the system. According to studies, the distribution level takes out 13% of the power generated by the generating level. The main reason of feeder losses is reactive current flow in the distribution line. Thus, it is an important task to reduce reactive current in the feeder, to improve bus voltage, and to reduce the losses of the feeder. Capacitors are one of the most effective options to compensate reactive power and to improve voltage regulation. The thesis proposes the combination of genetic algorithm and simulated annealing as optimal method to find out the optimal location and size of the switched capacitors in order to reduce the total cost of capacitors and of feeder losses. To find out the changes of cost between before and after capacitor compensated, IEEE 69-bus distribution system was used to test in this thesis. It proves that the combination of genetic algorithm and simulated annealing is the most effective method to solve the combinatorial capacitor compensated problem.
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Chen, Liu-Hsun, and 陳柳勳. "A Simulated Annealing Based Algorithm for the Hub Location Problem." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/47680980212477276294.
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碩士大葉大學
工業工程學系碩士班
92
The location of hub facilities is fundamental to the design of a “hub-and-spoke network”, as it greatly affects the total transportation costs of system. Hub is a special type of facility, which is designed to act as switching point for internal flows of a network. The problem is to determine the optimal location for the hubs and the assignments of the nonhubs to hubs that minimizes the overall transportation cost. Due to its characteristics of NP-Complete, it is difficult to obtain optimal solution within an acceptable amount of time. In this study we consider both single and multiple allocation hub location problems. A hybrid heuristic algorithm based on the SAHUB, combining with simulated annealing(SA) and Tabu list, is developed. During the research, the parameters used in the heuristics that affect the solution quality and efficiency are analyzed and designed with a good parameter setting suggested. The SAHUBR algorithm presented in this study compares favorably with the computational results obtained from the literature. Experimental results indicate that SAHUBR is capable of obtaining good solutions in a very small amount of time.
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WU, MENG-JU, and 吳孟儒. "A Lane Detection System Based on Sequential Simulated Annealing Algorithm." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/99607571902726759459.
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碩士中華大學
電機工程學系
104
A lane detection system that is based on the sequential simulated annealing algorithm is proposed in the thesis. Experimental results show that the correct recognition rate of the sequential simulated-annealing-algorithm-based lane detection system can reach 90%. Compared to the popular Hough transform algorithm, the simulated annealing algorithm uses fewer memories. It consumes about half of the memories used in the Hough transform method. Therefore, the proposed system can alleviate the problem of a large memory demand in Hough-transform-based systems, and it is of much potential in commercial applications. In our research, a dash cam installed in a car was used to record the road images in front of the car. Day-time and night-time still color images on freeways are used as the inputs of the proposed system. The research goal is to effectively detect the lane side lines to be used for improving driving safety. The pre-processing technique in the proposed system includes color to gray-level transformation, binarization, Sobel edge detection, morphology operations (erosion and dilation), and noise removal. After pre-processing, a binary lane image with some noise is obtained, and the sequential simulated annealing pattern detection method is applied for detecting lane position. The sequential simulated annealing pattern detection method can determine a set of parameter vectors with global minimal error. The detected patterns are removed from the binary image, and the remaining binary image is continue to be processed by using the sequential simulated annealing pattern detection method until all patterns are processed completely.
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Chang, Chia-Chun, and 張嘉君. "A study on multi-resource scheduling optimization using Simulated Annealing algorithm." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/45315841308659371356.
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碩士朝陽科技大學
營建工程系碩士班
91
Abstract The goal for resource scheduling is to have all of the activities get their own resources, and then the project can be completed before the established deadline. In order to reduce the delay of the project and satisfy the requirements of real world, it is necessary to make all the resource applied in the project be efficiently allocated and utilized under the condition of resources constraint. Traditionally, mathematical models of planning are employed to find the optimal solution for project scheduling under resource constrains. However, the employed models are complicated, and hung computational burdens are usually needed. It is difficult to find the analytical solution. For those problems of project scheduling under resource constrains, it is difficult to find a solution to satisfy the requirements of limited resources because of huge searching space. Therefore, how to find the best approximation instead of real one for those problems is an important research topic. In 1953, simulated annealing algorithm has been proposed. The basic idea of the simulated annealing is to establish an object function to simulate the energy condition, and finds a set of solutions from search space with modifying the object function when the energy can reach minimum. In doing so, the energy of the object function can move toward global minimum instead of local minimum condition. Therefore, with simulated annealing algorithm, it is possible to locate the best solution. The research explores the feasibility of applying simulated annealing algorithm to find a solution with considering the project scheduling under constrained resources. The research found out that employing simulated annealing algorithm could solve the multi-resource constraint problems for construction project. The research results demonstrate that applying simulated annealing algorithm do locate a reasonable approximation. Furthermore, it is possible to find out the optimal solution in search space.
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Hsu, Ku-Hui, and 許顧輝. "Study of Simulated Annealing Algorithm for Design of Diffractvie Optical Elements." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/78gc7j.
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碩士國立臺北科技大學
光電工程系所
93
In this thesis, we discussed several issues regarding the designs of the 4-level phase-only diffractive optical elements (DOEs) using the simulated annealing (SA) method. The SA scheme adopted here was the fast simulated annealing. The concerned issues include the effects of the initial and freezing temperatures, the stable state, and the number of perturbations. The simulation results of 4-level DOEs that were designed using various temperatures and stable states were presented and analyzed. Moreover, we discuss the relationship between annealing parameters and convergent properties. In the currently-presented papers about DOEs designed using the SA method, researchers adopted a conventional SA scheme in which a single perturbation is conducted in each iteration. This results on a limited search in a local area of the solution space. In this study, we proposed a modified SA scheme in which multiple perturbations were conducted to relax the local search on the conventional method. According to the simulation results, the case of multiple perturbation effectively extended the limited search area in the solution space, which in turns increased the convergence speed.
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Wu, Kuo-Huai, and 巫國槐. "Developing Genetic Simulated Annealing K-means Algorithm for Suppliers Cluster Analysis." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/a98q3t.
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碩士國立臺北科技大學
工業工程與管理研究所
96
The manufacturing industry is shifting to make-to-order pattern with the increasingly widened and diversified product application. To effectively segment and select suppliers, genetic algorithm (GA) was utilized to search for global solution and simulated annealing algorithm (SA) for local solution, so as to enhance the defective clustering results due to the randomly produced center of gravity by K-means, a genetic simulated annealing K-means algorithm (GSKA) was developed. After identifying all suppliers, then suppliers were clustered according to the characteristics in respect to customer needs using GSKA, and each cluster was then denominated in accordance to the characteristics in respect to customer needs. To prove the efficiency of this model, a case with desk computers was introduced, where GSKA and Taguchi method were utilized to cluster parts suppliers, and prove that the GSKA is superior to the K-means, SKA and GKA model in clustering results.