Sistema imunologico artificial para otimização multiobjetivo / Artificial immune system for multiobjetive optimization
AUTOR(ES)
Priscila Cristina Berbert
DATA DE PUBLICAÇÃO
2008
RESUMO
The aim of this work is to explore an Artificial Immune System, based on the Clonal Selection principle, in the solution of Multiobjective Optimization problems. Artificial Immune Systems have, in their elementary structure, the main characteristics required to solve Multiobjective Optimization problems: exploration, exploitation, paralelism, elitism, memory, diversity, mutation and proliferation proportional to the affinity, and dynamic repertorie. The proposed algorithm uses the Pareto dominance concept and feasibility to identify the antibodies (solutions) that must to be cloned. In the experiments, some important situations that occurs in real problems were considered: the presence of constraints (linear and non-linear) and Pareto Front format (convex, concave, continuous, discontinuous, discrete, non-uniforme). In the major part of the problems, the algorithm obtains good and competitive results when compared with approaches from the literature. Keywords: Multiobjective Optimization, Bio-inspired Algorithms, Artificial Immune Systems, Clonal Selection
ASSUNTO(S)
otmização matematica clonal selection sistema imune multiobjective optimization algoritmos evolutivos bio-inspired algorithms inteligencia artificial artificial immune systems
ACESSO AO ARTIGO
http://libdigi.unicamp.br/document/?code=vtls000440407Documentos Relacionados
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