Ponencia
Multi-Objective Particle Swarm Optimizers: An Experimental Comparison
Autor/es | Durillo, Juan J.
García Nieto, José Manuel Nebro, Antonio J. Coello Coello, Carlos A. Luna, Francisco Alba, Enrique |
Departamento | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial |
Fecha de publicación | 2009 |
Fecha de depósito | 2021-05-11 |
Publicado en |
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ISBN/ISSN | 978-3-642-01019-4 0302-9743 |
Resumen | Particle Swarm Optimization (PSO) has received increasing
attention in the optimization research community since its first appearance
in the mid-1990s. Regarding multi-objective optimization, a considerable
number of ... Particle Swarm Optimization (PSO) has received increasing attention in the optimization research community since its first appearance in the mid-1990s. 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 MOPSO version 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. |
Agencias financiadoras | Junta de Andalucía |
Identificador del proyecto | P07- TIC-03044 |
Cita | Durillo, J.J., García Nieto, J.M., Nebro, A.J., Coello Coello, C.A., Luna, F. y Alba, E. (2009). Multi-Objective Particle Swarm Optimizers: An Experimental Comparison. En EMO 2009: 5th International Conference on Evolutionary Multi-Criterion Optimization (495-509), Nantes, France: Springer. |
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