A particle swarm optimizer for multi-objective optimization

• Autores: Leticia Cagnina, Susana Cecilia Esquivel, Carlos Coello Coello
• Localización: Journal of Computer Science and Technology, ISSN-e 1666-6038, Vol. 5, Nº. 4, 2005 (Ejemplar dedicado a: Sixteenth Issue), págs. 204-210
• Idioma: inglés
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• Resumen

  • This paper proposes a hybrid particle swarm approach called Simple Multi-Objective Particle Swarm Optimizer (SMOPSO) which incorporates Pareto dominance, an elitist policy, and two techniques to maintain diversity: a mutation operator and a grid which is used as a geographical location over objective function space. In order to validate our approach we use three well-known test functions proposed in the specialized literature. Preliminary simulations results are presented and compared with those obtained with the Pareto Archived Evolution Strategy (PAES) and the Multi-Objective Genetic Algorithm 2 (MOGA2). These results also show that the SMOPSO algorithm is a promising alternative to tackle multiobjective optimization problems.