Controle de Inércia para Fuga de Mínimos Locais de Funções Não-Lineares na Otimização por Enxame de Partículas
Resumo
Este trabalho apresenta um mecanismo para reduzir as chances do processo de otimização de funções não-lineares estacionar em mínimos locais, ao utilizar a meta-heurística Otimização por Enxame de Partículas. Tal mecanismo trata-se de uma forma não-monotônica de controlar a inércia da partícula, que é um dos fatores responsáveis pela movimentação dessa durante o processo de otimização. Os resultados experimentais foram comparados com o modelo original da PSO padrão a fim de mostrar o potencial em encontrar uma melhor solução em funções de benchmark, para problemas complexos.
Referências
Coelho, C.A., Toscano, G. e Lechuga, M.S. (2004), Handling Multiple Objectives with Particle Swarm Optimization. IEEE Transactions on Evol. Computation, Vol. 8, n° 3.
Shi, Y. e Eberhart, R. C. (1999) “Empirical Study of Particle Swarm Optimization”, Proceedings of the 1999 Congress of Evolutionary Computation, vol. 3, 1945–1950.
Kennedy, J. e Eberhart, R. C. (1995), Particle swarm optimization. Proc. IEEE International Conference on Neural Networks (Perth, Australia), IEEE Service Center, Piscataway, NJ, pp. IV: 1942-1948.
Kennedy, J., Eberhart, R. e Shi, Y. (2001), Swarm Intelligence, Morgan Kaufmann Publishers. Inc, San Francisco, CA, 2001.
Eberhart, R. C. e Shi, Y. H. (1998), Comparison between genetic algorithms e particle swarm optimization. Annual Conference on Evolutionary Programming, San Diego.
Angeline, P. J. (1998), Using selection to improve particle swarm optimization. IEEE International Conference on Evolutionary Computation, Anchorage, Alaska, May 49.
Shi, Y. e Eberhart, R. C. (1998) “Parameter Selection in Particle Swarm Optimization”, Evolutionary Programming VII, Lecture Notes in Computer Science 1447, 591–600.
Suganthan, P. N. (1999) “Particle Swarm Optimiser with Neighbourhood Operator”, Proceedings of the 1999 Congress of Evolutionary Computation, vol. 3, 1958–1962.
Kennedy, J. (1999) “Small Worlds e Mega-Minds: Effects of Neighborhood Topology on Particle Swarm Performance”, Proceedings of the 1999 Congress of Evolutionary Computation, vol. 3, 1931–1938. IEEE Press.
Lvbjerg, M., Rasmussen, T. K. e Krink, T. (2001) “Hybrid Particle Swarm Optimiser with Breeding e Subpopulations”, Proceedings of the Genetic e Evolutionary Computation Conference.
Oliveira, H. C. B., Vasconcelos, G. C., Mesquita, R. V. e Souza, M. M. (2006), Parametrical Adjusting of a Hybrid System for the Vehicle Routing Problem with Time Windows. HIS'06, Auckland, New Zealand. IEEE Computer Society.
Jiao, B., Lian, Z. e Gu, X. (2008) “A dynamic inertia weight particle swarm optimization algorithm” Chaos, Solitons & Fractals - vol. 37, Issue 3, August 2008, Pages 698-705
Yang, X., Yuan, J., Yuan, J. e Mao H. (2007) “A modified particle swarm optimizer with dynamic adaptation”, Applied Mathematics and Computation, vol. 189, Issue 2, June 2007, Pages 1205-1213
Shi, Y. e Eberhart, R. C. (1999) “Empirical Study of Particle Swarm Optimization”, Proceedings of the 1999 Congress of Evolutionary Computation, vol. 3, 1945–1950.
Kennedy, J. e Eberhart, R. C. (1995), Particle swarm optimization. Proc. IEEE International Conference on Neural Networks (Perth, Australia), IEEE Service Center, Piscataway, NJ, pp. IV: 1942-1948.
Kennedy, J., Eberhart, R. e Shi, Y. (2001), Swarm Intelligence, Morgan Kaufmann Publishers. Inc, San Francisco, CA, 2001.
Eberhart, R. C. e Shi, Y. H. (1998), Comparison between genetic algorithms e particle swarm optimization. Annual Conference on Evolutionary Programming, San Diego.
Angeline, P. J. (1998), Using selection to improve particle swarm optimization. IEEE International Conference on Evolutionary Computation, Anchorage, Alaska, May 49.
Shi, Y. e Eberhart, R. C. (1998) “Parameter Selection in Particle Swarm Optimization”, Evolutionary Programming VII, Lecture Notes in Computer Science 1447, 591–600.
Suganthan, P. N. (1999) “Particle Swarm Optimiser with Neighbourhood Operator”, Proceedings of the 1999 Congress of Evolutionary Computation, vol. 3, 1958–1962.
Kennedy, J. (1999) “Small Worlds e Mega-Minds: Effects of Neighborhood Topology on Particle Swarm Performance”, Proceedings of the 1999 Congress of Evolutionary Computation, vol. 3, 1931–1938. IEEE Press.
Lvbjerg, M., Rasmussen, T. K. e Krink, T. (2001) “Hybrid Particle Swarm Optimiser with Breeding e Subpopulations”, Proceedings of the Genetic e Evolutionary Computation Conference.
Oliveira, H. C. B., Vasconcelos, G. C., Mesquita, R. V. e Souza, M. M. (2006), Parametrical Adjusting of a Hybrid System for the Vehicle Routing Problem with Time Windows. HIS'06, Auckland, New Zealand. IEEE Computer Society.
Jiao, B., Lian, Z. e Gu, X. (2008) “A dynamic inertia weight particle swarm optimization algorithm” Chaos, Solitons & Fractals - vol. 37, Issue 3, August 2008, Pages 698-705
Yang, X., Yuan, J., Yuan, J. e Mao H. (2007) “A modified particle swarm optimizer with dynamic adaptation”, Applied Mathematics and Computation, vol. 189, Issue 2, June 2007, Pages 1205-1213
Publicado
20/07/2009
Como Citar
SILVEIRA, Tiago; OLIVEIRA, Humberto César Brandão de; SILVA, Luiz Eduardo da.
Controle de Inércia para Fuga de Mínimos Locais de Funções Não-Lineares na Otimização por Enxame de Partículas. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 7. , 2009, Bento Gonçalves/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2009
.
p. 112-121.
ISSN 2763-9061.
