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Previous issue date: 2013-10-25 / This thesis proposes an architecture of a new multiagent system framework for hybridization
of metaheuristics inspired on the general Particle Swarm Optimization framework (PSO). The
main contribution is to propose an effective approach to solve hard combinatory optimization
problems. The choice of PSO as inspiration was given because it is inherently multiagent, allowing
explore the features of multiagent systems, such as learning and cooperation techniques.
In the proposed architecture, particles are autonomous agents with memory and methods for
learning and making decisions, using search strategies to move in the solution space. The concepts
of position and velocity originally defined in PSO are redefined for this approach. The
proposed architecture was applied to the Traveling Salesman Problem and to the Quadratic Assignment
Problem, and computational experiments were performed for testing its effectiveness.
The experimental results were promising, with satisfactory performance, whereas the potential
of the proposed architecture has not been fully explored. For further researches, the proposed
approach will be also applied to multiobjective combinatorial optimization problems, which are
closer to real-world problems. In the context of applied research, we intend to work with both
students at the undergraduate level and a technical level in the implementation of the proposed
architecture in real-world problems / A presente tese prop?e uma arquitetura multiagente para hibridiza??o de metaheur?sticas, inspirada
na t?cnica de Otimiza??o por Nuvem de Part?culas, e tem como principal contribui??o a
proposta de uma abordagem efetiva para resolu??o de problemas de otimiza??o combinat?ria. A
escolha da Otimiza??o por Nuvem de Part?culas como inspira??o deu-se pelo fato desta t?cnica
ser inerentemente multiagente, permitindo explorar os recursos dos sistemas multiagente, tais
como as t?cnicas de aprendizado e coopera??o. Na arquitetura proposta, as part?culas s?o agentes
aut?nomos com mem?ria e m?todos de decis?o e de aprendizagem, utilizando estrat?gias de
busca para se moverem no espa?o de solu??es. Os conceitos de posi??o e velocidade, originalmente
definidos na Otimiza??o por Nuvem de Part?culas, s?o redefinidos para esta abordagem.
A arquitetura proposta foi aplicada ao Problema do Caixeiro Viajante e ao Problema Quadr?tico
de Aloca??o, realizando experimentos computacionais que comprovaram sua efetividade. Os
resultados dos experimentos foram bastante promissores, apresentando desempenho satisfat?rio,
considerando que o potencial da arquitetura proposta ainda n?o foi totalmente explorado. Em
pesquisas futuras, a abordagem proposta ser? aplicada a problemas de otimiza??o combinat?ria
multiobjetivo, os quais s?o mais pr?ximos aos problemas do mundo real. No ?mbito da pesquisa
aplicada, pretende-se trabalhar tanto com alunos em n?vel de gradua??o como em n?vel t?cnico
a aplica??o da arquitetura proposta em problemas pr?ticos do mundo real
| Identifer | oai:union.ndltd.org:IBICT/oai:repositorio.ufrn.br:123456789/17956 |
| Date | 25 October 2013 |
| Creators | Souza, Givanaldo Rocha de |
| Contributors | CPF:81652011749, http://lattes.cnpq.br/2888641121265608, Canuto, Anne Magaly de Paula, CPF:66487099449, http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4790093J8, Pozo, Aurora Trinidad Ramirez, CPF:55168760953, http://lattes.cnpq.br/2815946827655352, Ramos, Iloneide Carlos de Oliveira, CPF:24260142453, http://lattes.cnpq.br/0613948277011672, Goldbarg, Marco C?sar, CPF:25841025953, http://lattes.cnpq.br/1371199678541174, Delgado, Myriam Regattieri de Biase da Silva, CPF:58567275172, http://lattes.cnpq.br/4166922845507601, Gouv?a, Elizabeth Ferreira |
| Publisher | Universidade Federal do Rio Grande do Norte, Programa de P?s-Gradua??o em Sistemas e Computa??o, UFRN, BR, Ci?ncia da Computa??o |
| Source Sets | IBICT Brazilian ETDs |
| Language | Portuguese |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, info:eu-repo/semantics/doctoralThesis |
| Format | application/pdf |
| Source | reponame:Repositório Institucional da UFRN, instname:Universidade Federal do Rio Grande do Norte, instacron:UFRN |
| Rights | info:eu-repo/semantics/openAccess |
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