A filter-based artificial fish swarm algorithm for constrained global optimization: theoretical and practical issues

• Autor(a) principal: Rocha, Ana Maria A. C.
• Data de Publicação: 2014
• Outros Autores: Costa, M. Fernanda P., Fernandes, Edite Manuela da G. P.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Texto Completo: http://hdl.handle.net/1822/30773
• topic: Global optimization; Artificial fish swarm; Filter method; Stochastic convergence; Artificial fish swarm; Engenharia e Tecnologia::Outras Engenharias e Tecnologias; Science & Technology

This paper presents a filter-based artificial fish swarm algorithm for solving non- convex constrained global optimization problems. Convergence to an ε-global minimizer is guaranteed. At each iteration k, the algorithm requires a (ρ(k),ε(k))-global minimizer of a bound constrained bi-objective subproblem,where as k →∞ ,ρ(k) →0 gives the constraint violation tolerance and ε(k) → ε is the error bound defining the accuracy required for the solution.The subproblems are solved by a population-based heuristic known as artificial fish swarm algorithm. Each subproblem relies on the approximate solution of the previous one, randomly generated new points to explore the search space for a global solution, and the filter methodology to accept non-dominated trial points.Convergence to a (ρ(k),ε(k))-global minimizer with probability one is guaranteed by probability theory. Preliminary numeri- cal experiments show that the algorithm is very competitive when compared with known deterministic and stochastic methods.