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Marrè, Badalló Roser. "Implementation and Testing of Two Bee-Based Algorithms in Finite Element Model Updating." Thesis, KTH, Bro- och stålbyggnad, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-140846.
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Finite Element Model Updating has recently arisen as an issue of vast importance on the design, construction and maintenance of structures in civil engineering. Many algorithms have been proposed, developed and enhanced in order to accomplish the demands of the updating process, mainly to achieve computationally efficient programs and greater results.The present Master Thesis proposes two new algorithms to be used in Finite Element Model Updating: the Bees Algorithms (BA) and the Artificial Bee Colony algorithm (ABC). Both were first proposed in 2005, are based on the foraging behaviour of bees and have been proved to be efficient algorithms in other fields. The objective of this Master Thesis is, thus, to implement and to test these two newalgorithms in Finite Element Model Updating for a cantilever beam. The Finite Element Model and the algorithms are programmed, followed by the extraction of the experimental frequencies and the updating process. Results, comparison of these two methods and conclusions are given at the end of this report, as well as suggestions for further work.
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Hashim, Mohd Ruzaini. "Improved spiral dynamics and artificial bee colony algorithms with application to engineering problems." Thesis, University of Sheffield, 2018. http://etheses.whiterose.ac.uk/20175/.
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Lee, Jessica. "Vägplanering i dataspel med hjälp av Artificial Bee Colony Algorithm." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-11044.
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Artificial Bee Colony Algorithm är en algoritm som tidigare tillämpats på många numeriska optimeringsproblem. Algoritmen är dock inte vanligt förekommande vad gäller vägplanering i dataspel. Detta arbete undersöker ifall algoritmen presterar bättre än A* på fyra olika testmiljöer eftersom A* är en av de mest använda algoritmerna för vägplanering i dataspel och således en bra referens. De aspekter som jämförs vid mätningarna är algoritmernas tidsåtgång samt längden på de resulterande vägarna. En riktad slumpgenerering av vägar har implementerats till algoritmen som gör att den inte fungerar på djupa återvändsgränder. Algoritmen har också en roulettehjulsselektion samt egenskapen att kunna generera slumpade grannvägar till de som skapats hittills. Resultaten visar att Artificial Bee Colony Algorithm presterar betydligt sämre än A* och att algoritmen därför inte är en bättre algoritm för vägplanering i dataspel. Algoritmen har dock potential till att prestera bättre och fungera på återvändsgränder om man förbättrar dess slumpgenerering av vägar.
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Idachaba, Unekwu Solomon. "A bio-inspired cache management policy for cloud computing environments using the artificial bee colony algorithm." Thesis, University of Kent, 2015. https://kar.kent.ac.uk/57856/.
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Caching has become an important technology in the development of cloud computing-based high-performance web services. Caches reduce the request-response latency experienced by users and reduce workload on backend databases. Caches need a high cache-hit rate to be fit for purpose, and this is dependent on the cache management policy used. Existing cache management policies do not prevent cache pollution and cache monopoly. This lack of prevention impacts negatively on cache hit rates. This work presents a Bio-inspired Community-based Caching (BCC) approach to address these two problems, by drawing intelligence from users' access behaviour using the Quantity and Quality Aware Artificial Bee Colony (Q2-ABC) clustering algorithm to achieve high cache-hit rates. Q2-ABC is a redesigned Artificial Bee Colony (ABC) algorithm which is also presented in this work. It optimizes the quality of clusters produced by addressing the repetition in metric space searches, probability-based effort distribution, and limit of abandonment problems inherent in ABC. To evaluate the performance of BCC, two sets of experiments were performed. In the first set of experiments, the quality of clusters identified by Q2-ABC was between 15% and 63% better than ABC. The performance of Q2-ABC comes with a cost: additional storage (a maximum of 300 bytes in this experiment) to store indexes of searched metric space. In the second set of experiments, the cache-hit rate achieved by BCC was between 0.7% and 55% better than the others across most of the test data used. The cost associated with BCC performance includes additional memory requirement-a total of 1.7Mb in this experiment-for storing generated intelligence and processor cycle overhead for generating intelligence. The implication of these results are that better quality clusters are produced by avoiding repeated searches within a metric space, and that high cache-hit rate can be achieved by managing caches intelligently, an alternative to expanding them as is conventional for Cloud Computing based services.
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Santos, Daniela Scherer dos. "Bee clustering : um algoritmo para agrupamento de dados inspirado em inteligência de enxames." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2009. http://hdl.handle.net/10183/18249.
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Agrupamento de dados é o processo que consiste em dividir um conjunto de dados em grupos de forma que dados semelhantes entre si permaneçam no mesmo grupo enquanto que dados dissimilares sejam alocados em grupos diferentes. Técnicas tradicionais de agrupamento de dados têm sido usualmente desenvolvidas de maneira centralizada dependendo assim de estruturas que devem ser acessadas e modificadas a cada passo do processo de agrupamento. Além disso, os resultados gerados por tais métodos são dependentes de informações que devem ser fornecidas a priori como por exemplo número de grupos, tamanho do grupo ou densidade mínima/máxima permitida para o grupo. O presente trabalho visa propor o bee clustering, um algoritmo distribuído inspirado principalmente em técnicas de inteligência de enxames como organização de colônias de abelhas e alocação de tarefas em insetos sociais, desenvolvido com o objetivo de resolver o problema de agrupamento de dados sem a necessidade de pistas sobre o resultado desejado ou inicialização de parâmetros complexos. O bee clustering é capaz de formar grupos de agentes de maneira distribuída, uma necessidade típica em cenários de sistemas multiagente que exijam capacidade de auto-organização sem controle centralizado. Os resultados obtidos mostram que é possível atingir resultados comparáveis as abordagens centralizadas.Clustering can be defined as a set of techniques that separate a data set into groups of similar objects. Data items within the same group are more similar than objects of different groups. Traditional clustering methods have been usually developed in a centralized fashion. One reason for this is that this form of clustering relies on data structures that must be accessed and modified at each step of the clustering process. Another issue with classical clustering methods is that they need some hints about the target clustering. These hints include for example the number of clusters, the expected cluster size, or the minimum density of clusters. In this work we propose a clustering algorithm that is inspired by swarm intelligence techniques such as the organization of bee colonies and task allocation among social insects. Our proposed algorithm is developed in a decentralized fashion without any initial information about number of classes, number of partitions, and size of partition, and without the need of complex parameters. The bee clustering algorithm is able to form groups of agents in a distributed way, a typical necessity in multiagent scenarios that require self-organization without central control. The performance of our algorithm shows that it is possible to achieve results that are comparable to those from centralized approaches.
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Matakas, Linas. "Dirbtinės bičių kolonijos algoritmai ir jų taikymai skirstymo uždaviniams spręsti." Bachelor's thesis, Lithuanian Academic Libraries Network (LABT), 2013. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2013~D_20130729_150200-28811.
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Šiame darbe yra trumpai apžvelgiami dalelių spiečių sistemų algoritmai, skirstymo uždaviniai ir jų formuluotės, bei praktinės interpretacijos, plačiau apžvelgiami ir analizuojami dirbtinių bičių kolonijų algoritmai. Taip pat šiame darbe galima rasti dirbtinių bičių kolonijų algoritmo pritaikymą skirstymo uždaviniams spręsti, bei sukurtos programos skaičiavimo rezultatų analizę.This paper consists of short descriptions of swarm systems algorithms, assigment problems and longer overview of artificial bee colony algorithms and it‘s analysis. Moreover, you can find an Artificial Bee Colony Algorithm's Application to one of an Assigment Problems and it's computational results analysis.
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Kavaliauskas, Donatas. "Dirbtinės bičių kolonijos algoritmai ir jų taikymai maršrutų optimizavimo uždaviniams spręsti." Bachelor's thesis, Lithuanian Academic Libraries Network (LABT), 2013. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2013~D_20130729_153102-94516.
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Šiame darbe yra trumpai apžvelgiami dalelių spiečių sistemų algoritmai, maršrutų optimizavimo uždaviniai ir jų formuluotės, bei praktinės interpretacijos. Plačiau apžvelgiami dirbtinių bičių kolonijų algoritmai ir jų pritaikymas keliaujančio pirklio uždaviniams spręsti. Taip pat šiame darbe galima rasti dirbtinių bičių kolonijų algoritmo pritaikymą keliaujančio pirklio uždaviniams spręsti, bei sukurtos programos skaičiavimo rezultatų analizę.This paper consists of short description of swarm systems algorithms, route optimisation problems overview and longer description of artificial bee colony algorithms adaptation for traveling salesman problem. Moreover, you can find an artificial bee colony algorithm's application to traveling salesman problem and analysis of computational results.
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SOUZA, Viviane Lucy Santos de. "Uma metodologia para síntese de circuitos digitais em FPGAs baseada em otimização multiobjetivo." Universidade Federal de Pernambuco, 2015. https://repositorio.ufpe.br/handle/123456789/17339.
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Atualmente, a evolução na arquitetura dos FPGAs (Field programable gate arrays) permite que os mesmos sejam empregados em aplicações que vão desde a prototipação rápida de circuitos digitais simples a coprocessadores para computação de alto desempenho. Entretanto, a utilização eficiente dessas arquiteturas é fortemente dependente, entre outros fatores, da ferramenta de síntese empregada. O desafio das ferramentas de síntese está em converter a lógica do projetista em circuitos que utilizem de maneira efetiva a área do chip, não degradem a frequência de operação e que, sobretudo, sejam eficientes em reduzir o consumo de energia. Nesse sentido, pesquisadores e grandes fabricantes de FPGA estão, frequentemente, desenvolvendo novas ferramentas com vistas a esses objetivos, que se caracterizam por serem conflitantes. O fluxo de síntese de projetos baseados em FPGAs engloba as etapas de otimização lógica, mapeamento, agrupamento, posicionamento e roteamento. Essas fases são dependentes, de forma que, otimizações nas etapas iniciais produzem impactos positivos nas etapas posteriores. No âmbito deste trabalho de doutorado, estamos propondo uma metodologia para otimização do fluxo de síntese, especificamente, nas etapas de mapeamento e agrupamento. Classicamente, a etapa de mapeamento é realizada mediante heurísticas que determinam uma solução para o problema, mas que, não permitem a busca por soluções ótimas, ou que beneficiam um objetivo em detrimento de outros. Desta forma, estamos propondo a utilização de uma abordagem multiobjetivo baseada em algoritmo genético e de uma abordagem multiobjetivo baseada em colônia artificial de abelhas que, associadas a heurísticas específicas do problema, permitem que sejam obtidas soluções de melhor qualidade e que resultam em circuitos finais com área reduzida, ganhos na frequência de operação e com menor consumo de potência dinâmica. Além disso, propomos uma nova abordagem de agrupamento multiobjetivo que se diferencia do estado da arte, por utilizar uma técnica de predição e por considerar características dinâmicas do problema, produzindo circuitos mais eficientes e que facilitam a tarefa das etapas de posicionamento e roteamento. Toda a metodologia proposta foi integrada ao fluxo acadêmico do VTR (Verilog to routing), um projeto código aberto e colaborativo que conta com múltiplos grupos de pesquisa, conduzindo trabalhos nas áreas de desenvolvimento de arquitetura de FPGAs e de novas ferramentas de síntese. Além disso, utilizamos como benchmark, um conjunto dos 20 maiores circuitos do MCNC (Microelectronics Center of North Carolina) que são frequentemente utilizados em pesquisas da área. O resultado do emprego integrado das ferramentas frutos da metodologia proposta permite a redução de importantes aspectos pós-roteamento avaliados. Em comparação ao estado da arte, são obtidas, em média, redução na área dos circuitos de até 19%, além da redução do caminho crítico em até 10%, associada à diminuição na potência dinâmica total estimada de até 18%. Os experimentos também mostram que as metodologias de mapeamento propostas são computacionalmente mais custosas em comparação aos métodos presentes no estado da arte, podendo ser até 4,7x mais lento. Já a metodologia de agrupamento apresentou pouco ou nenhum overhead em comparação ao metodo presente no VTR. Apesar do overhead presente no mapeamento, os métodos propostos, quando integrados ao fluxo completo, podem reduzir o tempo de execução da síntese em cerca de 40%, isto é o resultado da produção de circuitos mais simples e que, consequentemente, favorecem as etapas de posicionamento e roteamento.
Nowadays, the evolution of FPGAs (Field Programmable Gate Arrays) allows them to be employed in applications from rapid prototyping of digital circuits to coprocessor of high performance computing. However, the efficient use of these architectures is heavily dependent, among other factors, on the employed synthesis tool. The synthesis tools challenge is in converting the designer logic into circuits using effectively the chip area, while, do not degrade the operating frequency and, especially, are efficient in reducing power consumption. In this sense, researchers and major FPGA manufacturers are often developing new tools to achieve those goals, which are characterized by being conflicting. The synthesis flow of projects based on FPGAs comprises the steps of logic optimization, mapping, packing, placement and routing. These steps are dependent, such that, optimizations in the early stages bring positive results in later steps. As part of this doctoral work, we propose a methodology for optimizing the synthesis flow, specifically, on the steps of mapping and grouping. Classically, the mapping step is performed by heuristics which determine a solution to the problem, but do not allow the search for optimal solutions, or that benefit a goal at the expense of others. Thus, we propose the use of a multi-objective approach based on genetic algorithm and a multi-objective approach based on artificial bee colony that, combined with problem specific heuristics, allows a better quality of solutions are obtained, yielding circuits with reduced area, operating frequency gains and lower dynamic power consumption. In addition, we propose a new multi-objective clustering approach that differs from the state-of-the-art, by using a prediction technique and by considering dynamic characteristics of the problem, producing more efficient circuits and that facilitate the tasks of placement and routing steps . The proposal methodology was integrated into the VTR (Verilog to routing) academic flow, an open source and collaborative project that has multiple research groups, conducting work in the areas of FPGA architecture development and new synthesis tools. Furthermore, we used a set of the 20 largest MCNC (Microelectronics Center of North Carolina) benchmark circuits that are often used in research area. The results of the integrated use of tools based on the proposed methodology allow the reduction of important post-routing aspects evaluated. Compared to the stateof- the-art, are achieved, on average, 19% reduction in circuit area, besides 10% reduction in critical path, associated with 18% decrease in the total dynamic estimated power. The experiments also reveal that proposed mapping methods are computationally more expensive in comparison to methods in the state-of-the-art, and may even be 4.7x slower. However, the packing methodology presented little or no overhead compared to the method in VTR. Although the present overhead mapping, the proposed methods, when integrated into the complete flow, can reduce the running time of the synthesis by approximately 40%, which is the result of more simple circuits and which, consequently, favor the steps of placement and routing.
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Duarte, Grasiele Regina. "Um algoritmo inspirado em colônias de abelhas para otimização numérica com restrições." Universidade Federal de Juiz de Fora (UFJF), 2015. https://repositorio.ufjf.br/jspui/handle/ufjf/3544.
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CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Os problemas de otimização estão presentes em diversas áreas de atuação da sociedade e o uso de algoritmos bio-inspirados para a resolução de problemas complexos deste tipo vem crescendo constantemente. O Algoritmo Colônia de Abelhas Artificiais (ABC – do inglês Artificial Bee Colony) é um algoritmo bio-inspirado proposto em 2005 para a resolução de problemas de otimização multimodais e multidimensionais. O fenômeno natural que inspirou o desenvolvimento do ABC foi o comportamento inteligente observado em colônias de abelhas, mais especificamente no forrageamento. O ABC foi proposto inicialmente para ser aplicado na resolução de problemas sem restrições. Este trabalho avalia o desempenho do ABC quando aplicado na resolução de problemas de otimização com restrições. Para o tratamento das restrições, métodos de penalização serão incorporados ao ABC. São analisados diversos métodos de penalização, de diferentes tipos, com o objetivo de identificar com qual deles o algoritmo apresenta melhor desempenho. Além disto, são avaliadas possíveis limitações e cuidados que devem ser tomados ao combinar métodos de penalização ao ABC. O algoritmo proposto é avaliado através da resolução de problemas de otimização encontrados na literatura. Vários experimentos computacionais são realizados e gráficos e tabelas são gerados para demonstração dos resultados obtidos que também são discutidos.
Optimization problems are present in several areas of society and the use of bio-inspired algorithms to solve complex problems of this type has been growing constantly. The Artificial Bee Colony Algorithm (ABC) is a bio-inspired algorithm proposed in 2005 for solving multimodal and multidimensional optimization problems. The natural phenomenon that inspired the development of the ABC was intelligent behavior observed in bee colonies, more specifically in foraging. The ABC was initially proposed to be applied to solve unconstrained problems. This study evaluates the performance of ABC when applied in solving constrained optimization problems. For the treatment of constraints, penalty methods will be incorporated into the ABC. Several penalty methods, of different types, are analyzed with the goal of identifying which of these penalty methods offers better performance. Furthermore, possible limitations and care that should be taken when combining penalty methods to ABC are evaluated. The proposed algorithm is evaluated by solving optimization problems found in the literature. Several computational experiments are performed and graphs and tables are generated for demonstration of the obtained results which are also discussed.
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Vladimir, Bugarski. "Ekspertski sistem za upravljanje brodskom prevodnicom zasnovan na računarskoj inteligenciji." Phd thesis, Univerzitet u Novom Sadu, Fakultet tehničkih nauka u Novom Sadu, 2015. http://www.cris.uns.ac.rs/record.jsf?recordId=95378&source=NDLTD&language=en.
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U disertaciji je dato jedno rešenje automatskog operativnogupravljanja dvosmernom brodskom prevodnicom sa jednom komorom.Kreiran je ekspertski sistem zasnovan na rasplinutoj (fuzzy) logici.Upravljački sistem je testiran na modelu brodske prevodnice koji jekreiran na osnovu statističkih podataka o gustini saobraćaja nahidrosistemu DTD (Dunav-Tisa-Dunav), na osnovu tehničkedokumentacije brodske prevodnice i na osnovu razgovora saoperaterima. Sistem je zatim optimizovan globalnim algoritmimaoptimizacije. Dobijeno rešenje se pokazalo značajno bolje u poređenjusa standardnim algoritmima odluke.This thesis presents a solution to automatic control of a two-way one-channelship lock. Expert system based on fuzzy logic is designed. This controlsystem is tested on model of ship lock created using statistical data oftransportation density on DTD (Danube-Tisa-Danube) channel, usingtechnical documentation of ship lock and interview with operators. Thesystem is further optimized with global optimization techniques. Givensolution proved to be significantly better than standard decision algorithms.
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Tatjana, Jakšić Krüger. "Development, implementation and theoretical analysis of the bee colony optimization meta-heuristic method." Phd thesis, Univerzitet u Novom Sadu, Fakultet tehničkih nauka u Novom Sadu, 2017. https://www.cris.uns.ac.rs/record.jsf?recordId=104550&source=NDLTD&language=en.
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The Ph.D. thesis addresses a comprehensive study of the bee colonyoptimization meta-heuristic method (BCO). Theoretical analysis of themethod is conducted with the tools of probability theory. Necessary andsufficient conditions are presented that establish convergence of the BCOmethod towards an optimal solution. Three parallelization strategies and fivecorresponding implementations are proposed for BCO for distributed-memorysystems. The influence of method’s parameters on the performance of theBCO algorithm for two combinatorial optimization problems is analyzedthrough the experimental study.Докторска дисертације се бави испитивањем метахеуристичке методеоптимизације колонијом пчела. Извршена је теоријска анализаасимптотске конвергенције методе посматрањем конвергенције низаслучајних променљивих. Установљени су довољни и потребни условиза које метода конвергира ка оптималном решењу. Предложене су тристратегије паралелизације и пет одговарајућих имплементација конст-руктивне варијанте методе за рачунаре са дистрибуираном меморијом.Извршено је експериментално испитивање утицаја параметара методена њене перформансе за два различита комбинаторна проблема:проблем распоређивања и проблем задовољивости.
Doktorska disertacije se bavi ispitivanjem metaheurističke metodeoptimizacije kolonijom pčela. Izvršena je teorijska analizaasimptotske konvergencije metode posmatranjem konvergencije nizaslučajnih promenljivih. Ustanovljeni su dovoljni i potrebni usloviza koje metoda konvergira ka optimalnom rešenju. Predložene su tristrategije paralelizacije i pet odgovarajućih implementacija konst-ruktivne varijante metode za računare sa distribuiranom memorijom.Izvršeno je eksperimentalno ispitivanje uticaja parametara metodena njene performanse za dva različita kombinatorna problema:problem raspoređivanja i problem zadovoljivosti.
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Melo, Everton Luiz de. "Meta-heurísticas Iterated Local Search, GRASP e Artificial Bee Colony aplicadas ao Job Shop Flexível para minimização do atraso total." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/3/3136/tde-15122014-002717/.
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O ambiente de produção abordado neste trabalho é o Job Shop Flexível (JSF), uma generalização do Job Shop (JS). O problema de programação de tarefas, ou jobs, no ambiente JS é classificado por Garey; Johnson e Sethi (1976) como NP-Difícil e o JSF é, no mínimo, tão difícil quanto o JS. O JSF é composto por um conjunto de jobs, cada qual constituído por operações. Cada operação deve ser processada individualmente, sem interrupção, em uma única máquina de um subconjunto de máquinas habilitadas. O principal critério de desempenho considerado é a minimização dos atrasos dos jobs. São apresentados modelos de Programação Linear Inteira Mista (PLIM) para minimizar o atraso total e o instante de término da última operação, o makespan. São propostas novas regras de prioridade dos jobs, além de adaptações de regras da literatura. Tais regras são utilizadas por heurísticas construtivas e são aliadas a estratégias cujo objetivo é explorar características específicas do JSF. Visando aprimorar as soluções inicialmente obtidas, são propostas buscas locais e outros mecanismos de melhoria utilizados no desenvolvimento de três meta-heurísticas de diferentes categorias. Essas meta-heurísticas são: Iterated Local Search (ILS), classificada como meta-heurística de trajetória; Greedy Randomized Adaptive Search (GRASP), meta-heurística construtiva; e Artificial Bee Colony (ABC), meta-heurística populacional recentemente proposta. Esses métodos foram selecionados por alcançarem bons resultados para diversos problemas de otimização da literatura. São realizados experimentos computacionais com 600 instâncias do JSF, permitindo comparações entre os métodos de resolução. Os resultados mostram que explorar as características do problema permite que uma das regras de prioridade propostas supere a melhor regra da literatura em 81% das instâncias. As meta-heurísticas ILS, GRASP e ABC chegam a conseguir mais de 31% de melhoria sobre as soluções iniciais e a obter atrasos, em média, somente 2,24% superiores aos das soluções ótimas. Também são propostas modificações nas meta-heurísticas que permitem obter melhorias ainda mais expressivas sem aumento do tempo de execução. Adicionalmente é estudada uma versão do JSF com operações de Montagem e Desmontagem (JSFMD) e os experimentos realizados com um conjunto de 150 instâncias também indicam o bom desempenho dos métodos desenvolvidos.The production environment addressed herein is the Flexible Job Shop (FJS), a generalization of the Job Shop (JS). In the JS environment, the jobs scheduling problem is classified by Garey; Johnson and Sethi (1976) as NP-Hard and the FJS is at least as difficult as the JS. FJS is composed of a set of jobs, each consisting of operations. Each operation must be processed individually, without interruption, in a single machine of a subset of enabled machines. The main performance criterion is minimizing the jobs tardiness. Mixed Integer Linear Programming (MILP) models are presented. These models minimize the total tardiness and the completion time of the last operation, makespan. New priority rules of jobs are proposed, as well as adaptations of rules from the literature. These rules are used by constructive heuristics and are combined with strategies aimed at exploiting specific characteristics of FSJ. In order to improve the solutions initially obtained, local searches and other improvement mechanisms are proposed and used in the development of metaheuristics of three different categories. These metaheuristics are: Iterated Local Search (ILS), classified as trajectory metaheuristic; Greedy Randomized Adaptive Search (GRASP), constructive metaheuristic, and Artificial Bee Colony (ABC), recently proposed population metaheuristic. These methods were selected owing to their good results for various optimization problems in the literature. Computational experiments using 600 FJS instances are carried out to allow comparisons between the resolution methods. The results show that exploiting the characteristics of the problem allows one of the proposed priority rules to exceed the best literature rule in about 81% of instances. Metaheuristics ILS, GRASP and ABC achieve more than 31% improvement over the initial solutions and obtain an average tardiness only 2.24% higher than the optimal solutions. Modifications in metaheuristics are proposed to obtain even more significant improvements without increased execution time. Additionally, a version called Disassembly and Assembly FSJ (DAFJS) is studied and the experiments performed with a set of 150 instances also indicate good performance of the methods developed.
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Nilsson, Mikael. "Parameter Tuning Experiments of Population-based Algorithms." Thesis, Mittuniversitetet, Institutionen för informationsteknologi och medier, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-13836.
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In this study, three different algorithms are implemented to solve thecapacitated vehicle routing problem with and without time windows:ant colony optimization, a genetic algorithm and a genetic algorithmwith self-organizing map. For the capacitated vehicle routing problemthe Augerat et al’s benchmark problems were used and for the capaci-tated vehicle routing problem with time windows the Solomon’sbenchmark problems. All three algorithms were tuned over thirtyinstances per problem with the tuners SPOT and ParamILS. The tuningresults from all instances were combined to the final parameter valuesand tested on a larger set of instances. The test results were used tocompare the algorithms and tuners against each other. The ant colonyoptimization algorithm outperformed the other algorithms on bothproblems when considering all instances. The genetic algorithm withself-organizing map found more best known solutions than any otheralgorithm when using parameters, on the capacitated vehicle routingproblem. The algorithms performed well and several new best knownresults were discovered for the capacitated vehicle routing problem andnew best solutions found by heuristics were discovered for the 100customer Solomon problems. When comparing the tuners they bothworked well and no clear winner emerged.
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Loubiere, Peio. "Amélioration des métaheuristiques d'optimisation à l'aide de l'analyse de sensibilité." Thesis, Paris Est, 2016. http://www.theses.fr/2016PESC1051/document.
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L'optimisation difficile représente une classe de problèmes dont la résolution ne peut être obtenue par une méthode exacte en un temps polynomial.Trouver une solution en un temps raisonnable oblige à trouver un compromis quant à son exactitude.Les métaheuristiques sont une classe d'algorithmes permettant de résoudre de tels problèmes, de manière générique et efficiente (i.e. trouver une solution satisfaisante selon des critères définis: temps, erreur, etc.).Le premier chapitre de cette thèse est notamment consacré à la description de cette problématique et à l'étude détaillée de deux familles de métaheuristiques à population, les algorithmes évolutionnaires et les algorithmes d'intelligence en essaim.Afin de proposer une approche innovante dans le domaine des métaheuristiques, ce premier chapitre présente également la notion d'analyse de sensibilité.L'analyse de sensibilité permet d'évaluer l'influence des paramètres d'une fonction sur son résultat.Son étude caractérise globalement le comportement de la fonction à optimiser (linéarité, influence, corrélation, etc.) sur son espace de recherche.L'incorporation d'une méthode d'analyse de sensibilité au sein d'une métaheuristique permet d'orienter sa recherche le long des dimensions les plus prometteuses.Deux algorithmes réunissant ces notions sont proposés aux deuxième et troisième chapitres.Pour le premier algorithme, ABC-Morris, la méthode de Morris est introduite dans la métaheuristique de colonie d'abeilles artificielles (ABC).Cette inclusion est dédiée, les méthodes reposant sur deux équations similaires.Afin de généraliser l'approche, une nouvelle méthode, NN-LCC, est ensuite développée et son intégration générique est illustrée sur deux métaheuristiques, ABC avec taux de modification et évolution différentielle.L'efficacité des approches proposées est testée sur le jeu de données de la conférence CEC 2013. L'étude se réalise en deux parties: une analyse classique de la méthode vis-à-vis de plusieurs algorithmes de la littérature, puis vis-à-vis de l'algorithme d'origine en désactivant un ensemble de dimensions, provoquant une forte disparité des influencesHard optimization stands for a class of problems which solutions cannot be found by an exact method, with a polynomial complexity.Finding the solution in an acceptable time requires compromises about its accuracy.Metaheuristics are high-level algorithms that solve these kind of problems. They are generic and efficient (i.e. they find an acceptable solution according to defined criteria such as time, error, etc.).The first chapter of this thesis is partially dedicated to the state-of-the-art of these issues, especially the study of two families of population based metaheuristics: evolutionnary algorithms and swarm intelligence based algorithms.In order to propose an innovative approach in metaheuristics research field, sensitivity analysis is presented in a second part of this chapter.Sensitivity analysis aims at evaluating arameters influence on a function response. Its study characterises globally a objective function behavior (linearity, non linearity, influence, etc.), over its search space.Including a sensitivity analysis method in a metaheuristic enhances its seach capabilities along most promising dimensions.Two algorithms, binding these two concepts, are proposed in second and third parts.In the first one, ABC-Morris, Morris method is included in artificial bee colony algorithm.This encapsulation is dedicated because of the similarity of their bare bone equations, With the aim of generalizing the approach, a new method is developped and its generic integration is illustrated on two metaheuristics.The efficiency of the two methods is tested on the CEC 2013 conference benchmark. The study contains two steps: an usual performance analysis of the method, on this benchmark, regarding several state-of-the-art algorithms and the comparison with its original version when influences are uneven deactivating a subset of dimensions
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Ittner, Alexandre Erwin. "Implementação e avaliação de abordagens heurísticas para o problema do roteamento de cabos em painéis elétricos." Universidade do Estado de Santa Catarina, 2010. http://tede.udesc.br/handle/handle/1906.
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This dissertation presents a research work on the Cable Routing Problem in Electrical Panels and its resolution by computational means. Strictly, this work shows a formal definition for the problem, elaborates on the available computational approaches, and suggests several algorithms for its resolution. At last, an application developed using the proposed algorithms is described, yielding good results for the problem instances typically found in the industry.
Esta dissertação apresenta um estudo sobre as características do Problema do Roteamento de Cabos em Painéis Elétricos e sua solução por meios computacionais. Especificamente, este trabalho apresenta uma definição formal para o problema, descreve as abordagens computacionais disponíveis e propõe uma série de algoritmos para sua solução. Por fim, descreve-se um aplicativo desenvolvido empregando os algoritmos propostos que permite a obtenção de bons resultados para as instâncias deste problema tipicamente encontradas na indústria.
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Liu, Chia-Chien, and 劉佳倩. "Artificial Bee Colony Algorithms for Portfolio Optimization Problems." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/04193596577989134590.
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碩士元智大學
工業工程與管理學系
99
As global economy of today is slowdown and meager salary and interest of term deposit can’t adjust to the impact of inflation, financial investment has become an important focus of public concern. Artificial Bee Colony (ABC), one of metaheuristic algorithms, has attracted lots of attention in optimization field in recent years. ABC, employing the idea of swarm intelligence, simulates the principle of bee foraging behavior in the nature. Thus, this study adopts ABC to solve Portfolio Optimization Problem, and to provide effective portfolio as a reference basis of investment for investors. This study, based on Markowitz’s famous Mean-Variance Portfolio Model (M-V Model), aims at considering Downside Standard Deviation (DSD) and Upside Standard Deviation (USD) respectively and modifying the M-V model to fit the demands of different types of investors. Several well-known stock market indexes over different periods of time are tested to verify the modified models and the performance of the proposed ABC algorithms. The results are also compared with the ones obtained by Variable Neighborhood Search (VNS), Simulated Annealing (SA) and Tabu Search (TS) algorithms in the literatures. The results show that ABC performs better in terms of diversity, convergence, and effectiveness, particularly in ABC II when a Pareto front selection strategy is considered. Thus, ABC in this study is suitable for solving Portfolio Optimization Problem, and is able to provide valuable portfolio for investors with different attributes.
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Cho, Tzu-Ling, and 卓子菱. "Fuzzy Artificial Bee Colony Algorithm." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/nb7jvw.
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碩士義守大學
資訊管理學系
105
This study discussed Artificial Bee Colony Algorithm(ABC) was proposed by Karaboga in 2005. This optimization algorithm has a stable convergence rate, solving capabilities and an amount of control parameters. Though ABC has many advantages, there are still has some problems, such as the regional situation and the slow convergence. Therefore, this study refers to the research of Improved ABC, presented Fuzzy Artificial Bee Colony Algorithm(FABC). This research utilized fuzzy theory and Differential Evolution to improve the searching capability and falling into local optimal solution. In addition, the FABC is combined with GABC(FGABC), which is expected to improve the accuracy of the solution. From the experimental results, it is show that FABC and FGABC have improved the situation of falling into the regional solution, so that it can be continuous and effective development.
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Jhang, Rong-Zuo, and 張榮座. "Using Improved Artificial Bee Colony Algorithms for Neural Fuzzy Networks." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/83gj7p.
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碩士國立虎尾科技大學
電機工程研究所
101
This dissertation proposes two algorithms for neural fuzzy networks (NFN) in nonlinear control problem. The two algorithms are including the improved artificial bee colony (IABC) algorithm, and improved artificial bee colony with chaotic map (IABC_CM) algorithm. This dissertation consists of the two major parts. In the first part, the IABC method is proposed for the NFN model. The IABC adopts operator of differential evolution (DE) as the search strategy of artificial bee colony (ABC) to generate new solution and uses greedy selection to decide better solution for employed and onlooker bees. Furthermore, the IABC also uses a reward-based roulette wheel selection will be initially to divide all solutions suitably into feasible and infeasible solutions; thereafter, it divides them based on feasible and infeasible solutions for the implementation of incentives and punishments. In the second part, the IABC_CM is presented to balance the exploration and exploitation of the IABC algorithm effectively. The IABC_CM combines DE operator and migrate operator of BBO to produce new solution for employed bees. In order to avoid IABC method trapped into local optimum, the IABC_CM is utilized chaotic map to solve the above problem. Finally, the proposed two algorithms are applied to implement NFN model in various nonlinear control system problems. The results of this dissertation demonstrate the effectiveness of the proposed algorithms.
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Lee, Yuan-Chieh, and 李元傑. "Enhanced Artificial Bee Colony Algorithm with Centroid Strategy." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/36073207169039210717.
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碩士中原大學
資訊管理研究所
99
Artificial Bee Colony (ABC) was proposed in 2005 by the Karaboga, it has a very good stability and solves capability, the control parameter is few, computing simple and easy to implement. ABC is also already studied by several experiments, many scholars have been affirmed, and gradually be taken seriously, and have been widely used in various optimization problems. In recent years, scholars also start falling overboard for improvement ABC, making it become a popular object of study in recent years. ABC, while having very good stability, but there are still premature convergence, development issues such as poor accuracy. In this study, a number of scholars in reference to the literature related improvements, we found the centroid of swarm-based improvement methods, it development is still not mature. Therefore, this study proposed a new centroid of swarm-based improvement methods to try to strengthen the development capability of ABC. In this study, a total of 22 test functions looking to experiment, learn from the results of this study proposed improvement methods, can indeed effective in enhancing the development capability of ABC, make it can sustainable development without premature convergence, in performance on most test functions have improved significantly. in addition to able to obtain better results, but also to enhance its stability. In this study, we also applied our improvement methods to improved ABC of Other scholars proposed, can really also strengthen its capability, and can acquire better result.
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Huang, Shau-Tsuen, and 黃少村. "Enhanced Artificial Bee Colony Algorithm with Double Skips." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/78371714589155798976.
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碩士中原大學
資訊管理研究所
99
To optimize the artificial bee colony algorithm(ABC), this study proposed double skips and PSO’ bee strategy for modifications regarding problems, such as local optimization and inexact search of the algorithm. The double skips is consisted of the single-skip global exploration and the double-skips of in-depth exploitation. In terms of modifying employee bees of ABC, single skip can result in significant jump search of the bee colony to enable bee colony of moving failure to fly to new area for exploration by mapping of relative positions. Double skips is the exploitation of small scale to allow bees of poorer capabilities to search in area rich in foods according to globally optimized information. The double skips strategy can improve the mobility of the artificial bee colony, increase algorithm diversity and widen search range. The PSO’ bee strategy uses PSO directional concepts to improve onlooker bees of ABC using the difference gap between global optimization and neighboring bee colonies for guidance. As a result, the onlooker bees can absorb information from the two sources to improve search moves and increase the exploitation capability of algorithms.
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Huang, Jun-Xian, and 黃俊賢. "Digital Filter Design using Artificial Bee Colony Algorithm." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/19201496577064879745.
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碩士樹德科技大學
電腦與通訊系碩士班
100
This thesis focuses on the digital filter design based on using artificial bee colony (ABC) algorithm. The ABC algorithm is the optimization method of intelligent behavior and it is the population-based searching to fulfill the optimization purpose. In the algorithm, there are three different kinds of bees: employed bees, onlookers, and scouts. Each of food sources represents a possible solution to the optimized problem and the bees choose the food source according to the messages of itself and the partners. Digital filter is the discrete-time system with the desired response characteristics. It generally consists of two kinds of filters: FIR (finite impulse response) filter and IIR (infinite impulse response) filter. Each type has its individual merit and applicable place. Due to the advantages contained in FIR filter such as absolutely stable in nature and easy implementation, the main search topic of this thesis is concerned with the FIR digital filter design. We wish to design the high-pass and band-stop digital filters based on the proposed method. Numerical simulations reveal the effectiveness of the ABC-based method in designing the digital FIR filters.
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Tsai, Shang-Lin, and 蔡尚麟. "Objects Tracking Based on Artificial Bee Colony Algorithm." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/6gyu8r.
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碩士國立中央大學
電機工程學系
103
In recent years, as cameras and monitors become more and more popular, object tracking becomes a hot issue. In order to improve the accuracy of the tracking object and solve the occlusion problem, in this thesis, the Artificial Bee Colony (ABC) algorithm is used for object tracking in real time. In terms of object detection, in this thesis, the background subtraction is used for it can cut out complete targets, has low computation and be easily applied to real-time systems. Besides, the improved seed region growing method is used to distinguish every target and calculate its center. Then, for model building, color histograms are used to build target models. In order to avoid the interference of light, in this thesis, the HSV (Hue, Saturation and Value) color space is used. Moreover, for object tracking, in this thesis, the ABC algorithm which has a simple structure is used to find the best solution for it is easily used and its convergence is fast. Occlusion is always a big problem for object tracking. Therefore, in this thesis, the adaptive searching window is applied to exclude occlusion; the searching window will zoom in or out, depending on its fitness value. If the tracking window loses the targets, the searching window will increase. If the tracking window finds the targets, the searching window will adjust to the original size.
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Cheng, Hao-Wen, and 鄭皓文. "Adaptive Population Size of Artificial Bee Colony Algorithm." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/2zj623.
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碩士國立東華大學
電機工程學系
106
This thesis presents a novel adaptive population size of artificial bee colony algorithm (APSABC). The algorithm changes the population size to improve the search ability of the artificial bee colony algorithm. The purpose of adaptive population size is to use computing resources more effectively, and it has better performance than the original artificial bee colony algorithm, which has fixed population size. New bees are generated using optimal solution guidance and random bee average. The best solution guides the search ability around the best solution. The random bees develop other areas and have the opportunity to jump off the local best solution. In this study, the CEC2015 test functions, included unimodal functions, multimodal functions, hybrid functions and composition functions, are used to test the performance of APSABC and other ABC algorithms. The experiment results show that the proposed method has effective search ability than other algorithms.
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Han, Neysa Ignacia, and Neysa Ignacia Han. "Automatic Clustering using Improved Artificial Bee Colony Algorithm." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/92563657787932484120.
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碩士國立臺灣科技大學
工業管理系
101
Cluster analysis is an important technique in data mining. Many clustering algorithms have been proposed, but most of them need predetermined number of clusters. Unfortunately, unavailable information regarding number of clusters is commonly happened in real-world problems. Thus, this study intends to overcome this problem by proposing an algorithm for automatic clustering. The proposed algorithm is developed based on a population-based heuristic method, automatic clustering using improved artificial bee colony algorithm (ACIBC). It overcomes two main issues in automatic clustering, namely determining number of clusters and cluster centroids. In the automatic clustering using improved artificial bee colony, the exploration is conducted by solution which is comprised of two sections. Furthermore, sigmoid function is employed to handle infeasible solution. In addition, K-means algorithm is applied to adjust the cluster centroids. Method validation using four benchmark data sets reveals that overall ACIBC outperformed other two previous methods, namely DCPG and DCPSO, and also automatic clustering using original bee colony in terms of number of clusters, fitness value, accuracy and consistency.
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Chen, Jhih-Sian, and 陳志賢. "Dynamic Information Exchange based Artificial Bee Colony Algorithm." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/27413486050117817692.
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碩士亞東技術學院
資訊與通訊工程研究所
102
In the traditional artificial bee colony algorithm, bees only use one dimension when moving to find food sources. This type of movement leads to the bees’ obviously diminished ability to exchange information. In order to improve this problem, the dynamic information exchange based artificial bee colony algorithm was put forth in this study. In this paper, the dynamic information adjustment mechanism is proposed. A suitable number of dimensions will involve for bees’ movement in current generation according to solution searching status. It can be used to provide needed information for the bees’ movement, thereby enhancing their information exchange ability. In addition, in order to enhance bees’ search for better food sources when moving, the elite mechanism and the jumping mechanism were also proposed to improve accuracy of bees’ movement. The jumping mechanism, whose concept is similar to crossover mechanism of genetic algorithm, will force bees move to around the best bee. Further, the elite mechanism will make the best bee to provide its information with other bees and guide them toward to potential direction for following movement. In the experiments, the CEC 2005 test functions with 10, 30 and 50 dimensions are adopted to test the proposed method and compared it with six ABC related works. From the results, it can be observed that the proposed method performed better performance than six variants of ABC approaches. The proposed method performs excellent results on unimodal, multimodal and hybrid composition functions.
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Cai, Wan-Ting, and 蔡宛庭. "A Novel Artificial Bee Colony Algorithm with Diversity Strategy." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/42701301246113073530.
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碩士中原大學
資訊管理研究所
99
Artificial bee colony algorithm has been proven to be a simple structure, easy to use, rapid convergence speed, and it can be effectively applied to the complex optimization problem. However, there are problem of premature convergence and difficulties escaping the local optimum. In order to avoid those disadvantages, we propose a new diversity strategy improved ABC algorithm. Diversity strategy can be balanced between exploring and exploiting. The experimental results show, the proposed methods raised the performance of ABC algorithm.
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Yang, Ching-Yuan, and 楊清淵. "Improving Artificial Bee Colony Algorithm with Elite Escaping Strategy." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/47015184158563053537.
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碩士中原大學
資訊管理研究所
100
The artificial bee colony algorithm ,a novel algorithm in recent years, has been shown to be superior to Genetic algorithms, Particle Swarm Optimization algorithms, and Differential Evolution algorithm. However,there is still room for its improvement in accuracy and convergence rate; in addition, it also involves the issue in terms of the problem of the algorithm,which is easy to fall into the local optimal solution. This study proposes a search formula for improved employed bees and onlookers bee, by the use of optimal solution vector as well as individual optimal solution to guide the search direction, and to help search for solutions and the speed of convergence. In the scout bee,adding global optimal solutions and individual optimal solution vector to generate and using it to determine the threshold to avoid the problem of the local optimal solution,can effectively enhance the solution accuracy. This study consists in three experiments, including comparison with related researches, experimental noise, and non-0 the optimal solution. The main objective of the three experiments verifies the outstanding performance in different experimental environments. Different combination with other algorithms can also be examined in the future.
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Lin, Jeng Rung, and 林政融. "Artificial Bee Colony Algorithm to Construct of Classifier Ensemble." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/93959829488834424355.
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碩士華梵大學
資訊管理學系碩士班
102
Data classification method is one of the main tasks of data mining. In the literature, there are many classic base inducers used to train the classifier such as neural network, decision tree…etc., which are all individual classifier. In the past few years, many researches have proposed that the classifier ensemble, which composed by more than one individual classifier, is more effective than any individual classifier of the classifier ensemble. Inter-integrated classifier has diversity, with each classifier weights or non-weight samples of the new collective decisions to classify discrimination, but can't make the difference between classifier maximization. Because there are few literatures to research about how to optimize the diversity, this paper would propose an ensemble method（Artificial Bee Colony-Based Resampling by Diversity, ABCRD）that uses the genetic algorithm to encourage the diversity between classifiers by manipulating the train data set. We design an experiment using 15 UCI Repository of machine learning databases to test and verify and then comparing with individual classifier and other classifier ensembles. The result provides that the ABCRD in our experiment has better average accuracy（81.23%）and is significantly difference
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Chou, Yu-Wen, and 周于文. "Optimum Design of Structures by Artificial Bee Colony Algorithm." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/15938371420226511660.
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碩士淡江大學
航空太空工程學系碩士班
102
The Artificial Bee Colony (ABC) Algorithm was applied to the optimum design of structures in this study. The ABC algorithm is swarm intelligence based optimization technique inspired by the intelligent foraging behavior of honeybees. The advantage of ABC algorithm is quick convergence less settings of parameter, and extensive searching range. The employed bee and unemployed bee execute large range searching and the food source was chosen by the onlooker bee depending on the probability value associated with that food source. The FORTRAN and APDL of ANSYS software are integrated into a systematic ABC optimization program. The optimization problem can be transformed into a mathematical function. Minimum weight design will be developed in six numerical examples. Then the optimum deign of structures can be obtained by ABC algorithm. The results of ABC algorithm are better than other reference in the examples.
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Hsieh, Ming-Hsun, and 謝明勳. "A Novel Artificial Bee Colony Algorithm with Opposition-Based Learning." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/18220282148601992450.
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碩士中原大學
資訊管理研究所
100
The Artificial Bee Colony algorithm (ABC) popular in recent years evolutionary computation, the advantage of a simple parameter setting, high stability, fast convergence and high quality solutions, and can be applied to real world problems, initial Exploration of accuracy is not high in the solution process, however, vulnerable to the region the optimal solution can not jump out of the weak features of the solution space and the development stage. Opposition-based learning strategies improved Employee bees in this study, the introduction of the optimal solution to guide the improvement observed bee, combined with the leapfrog algorithm local evolution of improved detection of bee, The advantage of the formula is simple, fast execution speed, and then to explore the balance of capacity and development capabilities. This study recognized the function of the test and the non-zero optimal solution test functions have good performance, more suitable for complex real-world problems.
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Chiao, Huai-hsu, and 焦懷緒. "A Novel Artificial Bee Colony Algorithm with Nearest Neighbor Strategy." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/83020374608482285210.
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碩士中原大學
資訊管理研究所
100
The artificial bee colony algorithm (ABC) over the wide area search, and fast convergence shortcomings caused the search accuracy is not easy to fall into the region optimal solution (Karaboga &; Akay, 2009). In this paper, the recent bee strategy for improved mobile formulas and replacement policy to replace the scouts trip formula to improve the problems in this area. Bee strategy proposed in this study improved artificial bee colony algorithm (NCABC), confirmed through experiments that can effectively enhance the accuracy and stability of artificial bee colony algorithm. Whether the good results in the high dimensional or lower dimensional experiments. Compared with the journal Experimental and ABC prove that bee guide and replacement strategy can be seen in the test function, the noise test of the experimental data the mean and standard deviation improved significantly effect this study are non-zero optimal solution function good results. The artificial bee colony algorithm in mobile formula does not have a direction, do move through the vector generated by the randomly selected bees subtract such a way that randomness but changed to a rich diversity, have led to waste a lot of iteration number in the exploration section over a wide area search, lack of development of part of the cause is easy to fall into the region optimal solution; part of the scouts to make improvements, this study improved the scouts part.
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Chen, Jian-Ren, and 陳建任. "Protein Folding Simulation Using a Modified Artificial Bee Colony Algorithm." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/07709957462112842810.
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碩士朝陽科技大學
資訊工程系碩士班
97
In the computational biology, the prediction of proteins conformation from its amino acid sequence is one of the most prominent problems, solving the problem of protein structure is the most important works for study proteins, Hydrophobic-hydrophilic (HP) model is highly simplified model; it can represent behavioral properties of real-world proteins. Protein folding problem in the HP lattice model is the problem of finding the lowest free energy conformation. In order to enhance the performance of predicting protein structure, we propose a modified artificial bee colony (MABC) algorithm to determine the protein structure from sequence. We demonstrate that our algorithm can be applied successfully to the protein folding problem based on the hydrophobic-hydrophilic lattice model. Simulation results indicate that our approach performs better than those of existing evolutionary algorithms.
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Hsu, Ｋung-Shin, and 許恭實. "Fuel Cell Optimization Parameters Estimate by Artificial Bee Colony Optimization Algorithm." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/82919075614695213241.
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碩士逢甲大學
電機工程所
100
In response to the increasing depletion of natural resources on Earth, countries around the world all work actively in the development of green renewable energy. On one hand, the useful life of other sources can be extended; on the other hand, it can reduce the concentration of carbon dioxide in the air to avoid the ozone layer destruction. However, wind power, solar power and other renewable energy sources have intermittent feature, which has to be compatible with effective energy storage system to achieve higher economic efficiency. Due to fuel cells can improve energy efficiency, has storage function, and reduces environmental pollutions, it greatly improves the use of renewable energy application. Countries around the world work hard on developing this technology to make fuel cells more in line with popular demand. The article introduces several collective intelligence algorithms, the features of different fuel cells and theories. Next, the paper uses the application of artificial bee colony algorithm to estimate the optimal performance of fuel cells. Moreover, it changes the iteration of artificial bee colony algorithm to compare the differences of each iteration result. The estimated results of the article can provide the staff of fuel cells as a reference when design or use the fuel cells. Also, it is able to understand that the artificial bee colony algorithm is not only stable, but has fast convergence.
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Che-FuChiu and 邱哲夫. "An automatic clustering system with dynamic clustering artificial bee colony algorithm." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/90565365389297902538.
Full textAbstract:
碩士國立成功大學
資訊管理研究所
101
Clustering is an important technology in data mining. It is an unsupervised learning method. Clustering algorithm partitions input data into each cluster through similarity measure, where the data within a cluster are similar and the data in different clusters are dissimilar. Among many clustering algorithms, Metaheuristic clustering algorithms becomes popular recently in solving clustering problems. In different types of clustering methods, Metaheuristic clustering is a partition clustering method. To compare with the most well-known partition clustering algorithm－k-means algorithm, Metaheuristic clustering seems to have more stable and better performances. General clustering algorithms require users to provide prior knowledge of the dataset (e.g. the number of clusters). However, most datasets have high-dimensional, which makes the data hard to be observed, and thus the information (prior knowledge) is not easy to obtain. Therefore, it is important to develop a clustering algorithm which can automatically cluster the data and determine the number of clusters. With the success of Metaheuristic clustering, some researchers tend to design an automatic clustering algorithm with Metaheuristic method. For example, Genetic clustering for unknown K (GCUK) is based on Genetic algorithm (GA); and Multi-Elitist particle swarm optimization (MEPSO) is based on Particle swarm optimization (PSO). Though the two algorithms can successfully automatic clustering, their performances are not very good. Here are two possible reasons: (1) Encoding design is improper (2) The selected Metaheuristic algorithm does not suit the clustering problem or it lacks capability. This study proposes an Automatic Clustering System(ACS), which can automatically determine the number of clusters. First, ACS uses a Cluster Range Discovery algorithm(CRD) to reduce the search range of cluster number. Then, ACS uses Dynamic Clustering Artificial Bee Colony algorithm(DCABC) to complete the automatic clustering. DCABC adopts the Model Strategy to overcome the drawback of original Artificial bee colony algorithm (ABC). DCABC also designs a brand-new encoding format. Combining this encoding format, DCABC can cluster the data and find the number of clusters simultaneously. Finally, the performance of the DCABC was examined in the study.
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WANG, TING-BEN, and 王庭本. "Artificial Bee Colony Algorithm to Construct Non - Random Forest Classifier Ensemble." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/xr22td.
Full textAbstract:
碩士華梵大學
資訊管理學系碩士班
105
Data classification method is one of the main tasks of data mining. In the literature, there are many classic base inducers used to train the classifier such as neural network, decision tree…etc., which are all individual classifier. In the past few years, many researches have proposed that the classifier ensemble, which composed by more than one individual classifier, is more effective than any individual classifier of the classifier ensemble. A classifier ensemble is a set of classifiers that are diverse and yet accurate and individual decisions are combined in some way (typically by weighted or unweighted voting) to classify new examples. RF is an ensemble learning method that operates by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes output by individual trees.This paper would propose an ensemble method（Evolutionary Computation-based Non Random Forests, ECNRF）that uses the Artificial Bee Colony algorithm to encourage the diversity between classifiers by manipulating the train data set. We design an experiment using 15 UCI Repository of machine learning databases to test and verify and then comparing with individual classifier and other classifier ensembles. The result provides that the ECNRF in our experiment has better average accuracy（81.23%）and is significantly difference.
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Kao, Pei-Chun, and 高培鈞. "Artificial Bee Colony Algorithm for Multiple Objectives Optimal Power Dispatch Study." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/88w57z.
Full textAbstract:
碩士國立臺北科技大學
電機工程系所
105
The traditional economic dispatch method has considered a single object function which is to minimize the generation cost. In fact, there are more than one object with related constraints to consider meeting the power demand requirements. In practical system, the problems have more than one object function to be optimized. These objectives normally conflict each others. In recent years, due to the rise of environmental awareness. For example, we optimized the generation cost as main objective, it might cause other cost increasing if we neglect line loss and pollution emission. The more difficult part of the multi-objective optimization problem is conflicting between various objectives. It is hardly to assess its quality. Thus, this thesis has proposed using “double model algorithm” for optimal power flow in order to solve this problem. The artificial bee colony algorithm’s searching and sorting mechanism can handle this mutual non-dominated information capability and find out the most representative of the mutual non-dominated solution set. To sort the most optimal solution without setting weight situation by using data envelopment analysis method for this solution set with the efficiency concept. As a result, the validity of this thesis has been demonstrated through IEEE 30-Bus system simulation of the proposed algorithm.
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HUANG, SZU-CHI, and 黃思齊. "Artificial Bee Colony Algorithm for Solving an Operating Room Scheduling Problem." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/hrvm49.
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許晉瑜. "Optimizating Learning Disabilities Students Identification System by Using Artificial Bee Colony Algorithms With GA Evolution Strategy." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/81710967480835043134.
Full textAbstract:
碩士國立彰化師範大學
資訊管理學系所
102
The characteristics of students with learning disabilities (LDs) are not as obvious as other types of disabilities. Therefore, the process in identifying students LDs may be more difficult and require more resources, time and manpower. In order to reduce the evaluation personnel's workload, researchers have been using Artificial Neural Network (ANN) to develop AI-based LD student identification system. A previous research used general purpose graphics processing unit (GPGPU) to speed up the construction of LD identification model. The results have shown the execution speed is greatly improved, but the correct identification rate may still need a little improvement. Accordingly, in this study we use the Artificial Bee Colony (ABC) Algorithms with GA Evolution Strategy to optimize the training of the Artificial Neural Network (ANN) in constructing Learning Disabilities Students Identification Model. The experimental results show that, when compared with the Genetic Algorithm (GA), the use of the ABC with GA evolution strategy has improved the correct identification rate by as much as 1.8%, which is achieved without increasing the model construction time.
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Cheng-HsienChang and 張政賢. "A Learning Companion Recommendation Systemfor Facebook Using an Artificial Bee Colony Algorithm." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/84638718988371687108.
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Hung-ChangLi and 李弘彰. "A Dynamic Question Generation System on Facebook Using Artificial Bee Colony Algorithm." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/79420705219197964601.
Full textAbstract:
碩士國立成功大學
工程科學系專班
100
Facebook is currently one of the world's most popular social networking services, and has been widely used in the field of e-learning. In an e-learning environment, learners need a good and efficient way to assess their abilities and learning effectiveness. In this study, a Dynamic Question Generation System was proposed. Based on the theory of Computerized Adaptive Test, the proposed system used the artificial bee colony algorithm to find the suitable questions for each learner according to the learner's profile, and Facebook articles reading experience, professional ability, interest, and the attending e-learning records on the system. In order to evaluate the proposed system, the experiment used two question generation methods that were the dynamic question generation system and the traditional question generation system to explore the learner’s intention to use the proposed system by a questionnaire. The questionnaire consists of three main subjects: learning attitude, system satisfaction, and the technology acceptance model. The results show that the willingness of learner to use the DQGS has a positive impact on the integration of the system characteristics and Facebook.
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Chuang, Ya-Lan, and 莊雅嵐. "A Hybrid Artificial Bee Colony Algorithm for Vehicle Routing with Cross-docks." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/75793206230743780822.
Full textAbstract:
碩士國立暨南國際大學
資訊管理學系
101
Cross-docking is a one of the best pick-up and delivery strategies to reducing inventory costs and shortening the goods cycle. Cross-docking is for transition of goods rather than providing a storage place. The goods are unloaded from inbound vehicles and are sorted according to the destinations. The sorted goods are then uploaded to the outbound vehicles for delivery. In this study, we hybridize the artificial bee colony algorithm with three constraint-handling heuristics and two improvement local-search procedures. Following the mathematical model of the cross-docking problem, four types of test instances are generated. The experimental results show that our method outperforms the tabu search algorithm in the literature.
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Liu, Chia Ming, and 劉家銘. "Applying Local Search Based Artificial Bee Colony Algorithm to Nurse Rostering Problem." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/agfg9v.
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WANG, HAO-YU, and 王浩宇. "Investors' Appropriate Used in Stock Portfolio Strategy for Artificial Bee Colony Algorithm." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/jxwm48.
Full textAbstract:
碩士輔仁大學
資訊管理學系碩士在職專班
106
Information technology brings convenience to people, making investment tools more diversified and convenient, but for investors who generally do not have a financial and financial background, it is difficult to effectively integrate these tools and information to make appropriate Investment decisions, so choosing the right investment tools and commodities for you is very important. According to the above research questions, in this study, the individual's risk assessment is the target and combined with the algorithm to select the products suitable for the investors, and a set of systems to assist the investors in making decisions can be made, which can be effectively carried out according to the individual. Financial planning and investment decisions. In this study, we consider the long-term trend of stocks and the characteristics of investors to carry out investment portfolios to help investors find the investment targets that are suitable for them and can withstand risks. Through the bifurcation algorithm, it is applied to the investment period forecast of different lengths to help investors find the investment target with the best performance. This study takes Taiwan's 50 stocks as an example. Based on the attributes and preferences of investors, K-means and bee colony algorithm (ABC) are used to construct a portfolio strategy that constitutes investors' suitability, so as to screen out the most suitable investors. Good portfolio. The experimental results of this study confirmed that through the model of this experiment, three investment portfolios of investor attributes were selected, and the simulation investment was conducted during the training period and the test period to obtain the simulation experiment results. As a result, most of the experimental results of this experiment are in line with the needs of various types of investors. Although the results obtained are not the best benefits, the results are profitable. Therefore, the bee colony calculus is selected to suit the investor. The investment portfolio is in line with the needs of investors.
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Ye, Xuan-Bo, and 葉宣伯. "An Algorithm of Face Recognition Based on Artificial Bee Colony Algorithm and K-means." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/b87c84.
Full textAbstract:
碩士中原大學
電機工程研究所
102
In this thesis, we propose a face recognition method based on artificial bee colony algorithm and K-means. The main purpose is to hope that through this face recognition system, the face can be detected rapidly and framed up. We use this system to promote effectively the recognition rate of face recognition. In this thesis, we will present the architecture, methods and experimental results in our system. At first, we introduce our system architecture including image preprocessing, face detection, feature extraction and face recognition. Then we present the methods used in our research. They can be given into the following four steps. (1) We convert the color image into grayscale image and use histogram equalization method to adjust contrast. It can simplify the amount of image data and reduce the effect of light. (2) We can availably filtrate non-face images and find out the face area by AdaBoost method and use the image normalization to normalize the face part. (3) We can effectively reduce dimensions of image and retain the large variation of image features by using Principal Component Analysis (PCA) in feature extraction. (4) We combine Artificial Bee Colony algorithm combine with K-means to do face recognition. The ABC-K-means method can improve the weakness of local minimum and poor stability. This method can raise the robustness of K-means and face recognition rate. Finally, we use Matlab to simulate the proposed face recognition system and take ours method to compare with other methods mutually. The experiments prove that the face recognition system in this thesis can effectively increase recognition rate and the feasibility of the face recognition system in this thesis. In this thesis, the contributions of our research are as follows: 1. Artificial Bee Colony algorithm improves the weakness of local minimum and poor stability in K-means 2. The ABC-K-means method improves the face recognition rate to be better than other methods. 3. Our face recognition system can be used for access control systems, criminal investigation, 3C products, etc.
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Wu, Yi-Ren, and 吳易任. "A Novel Artificial Bee Colony Algorithm with Centroid Strategy and Opposition-Based Learning." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/r3n45w.
Full textAbstract:
碩士中原大學
資訊管理研究所
101
Artificial Bee Colony algorithm (ABC) is a collective intelligence algorithm which refers to bee swarm`s division of labor mode. Because ABC has more excellent performance than other algorithms especially the function optimization field, scholars have pay attention to it continuously since the first published. The distinctive features of ABC include simple calculation, easy to implement, and low demand parameter settings. However, there are still some defects in it, such as regional search which was unable to improve continuously, falling into local optimal solutions, and low effectiveness exploring of scout bee. In this study, we adopt the opposition of group center to lead search for strange area. This strategy is beneficial to adjust search resource of algorithm. It regulates exploring principle according to current trend of evolution, promoting convergence speed and increasing opportunity for departing from local optimal solutions. In this research, we use 5 generally acknowledged benchmark functions to test our method. The outcome demonstrates that our algorithm has good effect regardless of high and low dimension. It owns speedy and better resolving ability at multimodal functions.
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Wen, Jen-Chien, and 溫仁傑. "Study on Mobile Wireless Sensor Network Localization Using the Artificial Bee Colony Algorithm." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/t3cmfp.
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碩士國立臺北科技大學
電機工程系所
102
In recent years, smart mobile devices and the associated applications in various fields have been getting popular and many of them require the positioning function, for example: medical monitoring, mobile positioning and indoor navigation etc. Consequently, localization methods for wireless sensor networks play an important role in the aforementioned applications. In this thesis, a localization method for mobile sensor networks is presented. In order to reduce localization errors, the artificial bee colony (ABC) algorithm is used to modify the estimated coordinates. The fundamental concept of the ABC algorithm is to find better feasible solution (food source) from food sources in the specific space. According to the distance from food to bee colony and richness of pollen, the fitness function (profit function) is often used to calculate the fitness value of food (profitability) and these fitness values determine which food source to be collected. The food source with better fitness value replaces the original one. If the fitness value is not tolerable, then discard the food source and find a new target until the terminal condition is met. Using the idea, this study integrates the ABC algorithm and the improved Monte Carlo localization (IMCL) algorithm to improve the accuracy of positioning. The IMCL estimates the node coordinates and then the ABC refines the location. Also, different speeds, different number of nodes and degree of irregularity (DOI), and out of range prevention of coordinate estimation are considered in thesis. From the simulation results, it is seen that the proposed algorithm can effectively reduce the localization error. Comparing to the other Monte Carlo based localization algorithm MCL, MCB, and IMCL, the resulted estimation error is at most 0.44 times of communication range. It outperforms other three algorithms.
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Liu, Zhi Yuan, and 劉治元. "A Study of Applying Artificial Bee Colony Algorithm to Ultrasound Breast Tumor Image Diagnosis." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/46968407233110469967.
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碩士長庚大學
資訊管理學系
99
In recent years, the mortality from female breast cancer has gradually increased. The traditional tumor screening is time-consuming and invasive for patients. Now ultrasound imaging technology applying to tumor screening is convenient and fast, but ultrasound breast tumor images require experienced physicians to distinguish benign tumors from malignant ones. Hence, we propose a computer aided diagnosis (CAD) system which combines the artificial bee colony (ABC) algorithm and the support vector machine (SVM) to classify ultrasound breast tumor images in this study. This study evaluated 210 ultrasound breast tumor images including 120 benign tumors and 90 malignant ones. The breast tumor images first were processed by some image processing methods, such as noise reducing, edge enhancing, and automatic segmentation. The texture features and morphologic features were extracted following the use of an ABC algorithm to detect significant features and determine the optimal parameters for the support vector machine (SVM) to identify the tumor as benign or malignant. The experimental results show that the accuracy of the proposed CAD system for classifying breast tumors is 95.24%, the sensitivity is 97.78%. Because the proposed CAD system can differentiate benign from malignant breast tumors with high accuracy and short training time, it is therefore to reduce the number of clinically invasive examinations and assist the inexperienced physicians to avoid misdiagnosis.
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Lin, Pao-ching, and 林保慶. "Integration of Production Scheduling and Delivery in Supply Chain with Artificial Bee Colony Algorithm." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/anveqj.
Full textAbstract:
碩士國立雲林科技大學
工業工程與管理系
106
In the logistics center, workflow is a problem of integrated scheduling and delivery. There are many goods in the logistics center that are waiting for being processed. When the customer requests, the workers in the logistics center would start to work and decide the orders. Also, the workers would consider the vehicle’s capacity and plan a suitable route. After the goods in the logistics center finish processed, the workers would start to deliver. They must deliver them during the time window that the customer requests. If the goods are not delivered during the time window, there would be a penalty. Considering the situation of the traffic jam during rush hours, the decision-maker would choose the best path to save the cost. This research would consider the situations above all, and build a complete production scheduling and shipping mode with the goal of minimizing the total costs. This study would according to the problem and constructs a suitable mathematical model to solve the small-scale paradigm. Since this research belongs to the NP-hard problem, the solution time would grow exponentially on the large-scale problem. Therefore, by using Artificial bee colony (ABC) to solve, the aim is to find the best solutions in an acceptable time and give the research some advices to help the supervisor to refer.
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ZHANG, YU-CHENG, and 張昱晟. "Using Artificial Bee Colony algorithm to perform reactive power compensation for distribution power system." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/zdx9r6.
Full textAbstract:
碩士國立虎尾科技大學
電機工程系碩士班
107
Capacitors are often used in power systems to increase the voltage at the end of the feeder, improve the power factor and reduce the loss of feeder lines. This thesis uses the Artificial Bee Colony algorithm to perform the best capacitor device at the lowest cost. The main axis of this thesis is integrated with the power flow analysis and simulation platform software package Digsilent the mathematical calculation software Matlab. In this thesis the Artificial Bee Colony algorithm is used to simulate the virtual work compensation of the power distribution system. The actual processes of linking and transferring information between the above two software packages are introduced. In order to reduce the complexity of the operation and avoid the divergence phenomenon, the paper first selects the optimal position of the capacitor device to be selected by voltage sensitivity analysis, and then uses the artificial bee colony algorithm to select the optimal capacity. Finally, the Artificial Bee Colony algorithm is applied to the IEEE 13Bus radial system for optimal virtual work compensation, and its accuracy is verified by comparison with the Particle Swarm Optimization algorithm.
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Huang, Chao-Hsun, and 黃昭勳. "Optimum Design of Structures by an Hybrid Artificial Bee Colony and Differential Evolution Algorithm." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/98520821932683504720.
Full textAbstract:
碩士淡江大學
航空太空工程學系碩士班
103
An Hybrid Artificial Bee Colony (ABC) and Differential Evolution Algorithm (DE) was applied to the optimum design of structures in this study. The ABC algorithm is swarm intelligence based optimization technique inspired by the intelligent foraging behavior of honeybees. The advantages of ABC algorithm are quick convergence, less settings of parameter, easy to escape from the local optimal solution, and extensive searching range. Differential Evolution algorithm is an evolutional technique that has advantages of easy to implement, little parameter tuning requirement, using the difference calculation to enhance regional search capabilities, and also exhibits reliable, accurate and fast convergence. The advantage of hybrid ABC and DE algorithm is that the merit of both methods can be enhanced to overcome the disadvantage of each method. By using the characteristics of artificial bee colony algorithm which is easy to escape from the local optimal solution and the characteristics of differential evolution algorithm which is using the difference calculation to enhance regional search capabilities, the purpose of optimum design can be reached. The FORTRAN and APDL of ANSYS software are integrated into asystematic ABC-DE optimization program.The optimization problem can be transformed into a mathematical function. Then the optimum deign of structures can be obtained by ABC-DE algorithm. Minimum weight design will be demonstrated in six numerical examples. The results of ABC-DE algorithm are better than other references in the examples.