Multi-Objective Particle Swarm Optimizers: An Experimental Comparison

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Multi-Objective Particle Swarm Optimizers: An Experimental Comparison
Particle Swarm Optimization (PSO) has received increased attention in the optimization research community since its first appearance. Regarding multi-objective optimization, a considerable number of algorithms based on Multi-Objective Particle Swarm Optimizers (MOPSOs) can be found in the specialized literature. Unfortunately, no experimental comparisons have been made in order to clarify which version of MOPSO shows the best performance. In this paper, we use a benchmark composed of three well-known problem families (ZDT, DTLZ, and WFG) with the aim of analyzing the search capabilities of six representative state-of-the-art MOPSOs, namely, NSPSO, SigmaMOPSO, OMOPSO, AMOPSO, MOPSOpd, and CLMOPSO. We additionally propose a new MOPSO algorithm, called SMPSO, characterized by including a velocity constraint mechanism, obtaining promising results where the rest perform inadequately. Key words: Particle Swarm Optimization, Multi-Objective Optimization, Comparative Study
Juan José Durillo, José Garcí
Added 24 Jul 2010
Updated 24 Jul 2010
Type Conference
Year 2009
Where EMO
Authors Juan José Durillo, José García-Nieto, Antonio J. Nebro, Carlos A. Coello Coello, Francisco Luna, Enrique Alba
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