Chapter

Genetic and Evolutionary Computation – GECCO 2004

Volume 3102 of the series Lecture Notes in Computer Science pp 225-237

Using Clustering Techniques to Improve the Performance of a Multi-objective Particle Swarm Optimizer

  • Gregorio Toscano PulidoAffiliated withDepto. de Ing. Elect./Sección de Computación, CINVESTAV-IPN (Evolutionary Computation Group)
  • , Carlos A. Coello CoelloAffiliated withDepto. de Ing. Elect./Sección de Computación, CINVESTAV-IPN (Evolutionary Computation Group)

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Abstract

In this paper, we present an extension of the heuristic called “particle swarm optimization” (PSO) that is able to deal with multiobjective optimization problems. Our approach uses the concept of Pareto dominance to determine the flight direction of a particle and is based on the idea of having a set of sub-swarms instead of single particles. In each sub-swarm, a PSO algorithm is executed and, at some point, the different sub-swarms exchange information. Our proposed approach is validated using several test functions taken from the evolutionary multiobjective optimization literature. Our results indicate that the approach is highly competitive with respect to algorithms representative of the state-of-the-art in evolutionary multiobjective optimization.