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Particle swarm and simulated annealing for multi-global optimization

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Detalhes bibliográficos
Resumo:Particle swarm and simulated annealing optimization algorithms proved to be valid in finding a global optimum in the bound constrained optimization context. However, their original versions can only detect one global optimum even if the problem has more than one solution. In this paper we propose modifications to both algorithms. In the particle swarm optimization algorithm we introduce gradient information to enable the computation of all the global and local optima. The simulated annealing algorithm is combined with a stretching technique to be able to compute all global optima. The numerical experiments carried out with a set of well-known test problems illustrate the effectiveness of the proposed algorithms.
Autores principais:Vaz, Ismael
Outros Autores:Pereira, Ana I.; Fernandes, Edite M.G.P.
Assunto:Multi-global optimization Particle swarm optimization Simulated Annealing
Ano:2005
País:Portugal
Tipo de documento:comunicação em conferência
Tipo de acesso:acesso aberto
Instituição associada:Instituto Politécnico de Bragança
Idioma:inglês
Origem:Biblioteca Digital do IPB
Descrição
Resumo:Particle swarm and simulated annealing optimization algorithms proved to be valid in finding a global optimum in the bound constrained optimization context. However, their original versions can only detect one global optimum even if the problem has more than one solution. In this paper we propose modifications to both algorithms. In the particle swarm optimization algorithm we introduce gradient information to enable the computation of all the global and local optima. The simulated annealing algorithm is combined with a stretching technique to be able to compute all global optima. The numerical experiments carried out with a set of well-known test problems illustrate the effectiveness of the proposed algorithms.