Comparação de algoritmos de enxame de partículas para otimização de problemas em larga escala

• Ano de defesa: 2018
• Autor(a) principal: Melo, Leonardo Alves Moreira de
• Orientador(a): Cruz Junior, Gelson da
• Banca de defesa: Silva, Karina Rocha Gomes da, Rodrigues, Cássio Leonardo, Cruz Junior, Gelson da
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Goiás
• Programa de Pós-Graduação: Programa de Pós-graduação em Engenharia Elétrica e da Computação (EMC)
• Departamento: Escola de Engenharia Elétrica, Mecânica e de Computação - EMC (RG)
• País: Brasil
• Palavras-chave em Português: Enxame de partículas; PSO; Otimização
• Palavras-chave em Inglês: Particle swarm; PSO; Optimization
• Área do conhecimento CNPq: ENGENHARIAS::ENGENHARIA ELETRICA
• Link de acesso: http://repositorio.bc.ufg.br/tede/handle/tede/9108

In order to address an issue concerning the increasing number of algorithms based on particle swarm optimization (PSO) applied to solve large-scale optimization problems (up to 2000 variables), this article presents analysis and comparisons among five state- of-the-art PSO algorithms (CCPSO2, LSS- PSO, OBL-PSO, SPSO and VCPSO). Tests were performed to illustrate the e ciency and feasibility of using the algorithms for this type of problem. Six benchmark functions most commonly used in the literature (Ackley 1, Griewank, Rastrigin, Rosenbrock, Schwefel 1.2 and Sphere) were tested. The experiments were performed using a high-dimensional problem (500 variables), varying the number of particles (50, 100 and 200 particles) in each algorithm, thus increasing the computational complexity. The analysis showed that the CCPSO2 and OBL-PSO algorithms found significantly better solutions than the other algorithms for more complex multimodal problems (which most resemble realworld problems). However, considering unimodal functions, the CCPSO2 algorithm stood out before the others. Our results and experimental analysis suggest that CCPSO2 and OBL- PSO seem to be highly competitive optimization algorithms to solve complex and multimodal optimization problems.