Abstract

This paper presents an approach in which Pareto dominance is incorporated into particle swarm optimization (PSO) in order to allow this heuristic to handle problems with several objective functions. Unlike other current proposals to extend PSO to solve multiobjective optimization problems, our algorithm uses a secondary (i.e., external) repository of particles that is later used by other particles to guide their own flight. We also incorporate a special mutation operator that enriches the exploratory capabilities of our algorithm. The proposed approach is validated using several test functions and metrics taken from the standard literature on evolutionary multiobjective optimization. Results indicate that the approach is highly competitive and that can be considered a viable alternative to solve multiobjective optimization problems.

Keywords

Particle swarm optimizationMulti-objective optimizationMathematical optimizationMulti-swarm optimizationComputer scienceEvolutionary algorithmMetaheuristicEvolutionary computationOptimization problemPareto principleHeuristicImperialist competitive algorithmTest functions for optimizationMathematics

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Publication Info

Year
2004
Type
article
Volume
8
Issue
3
Pages
256-279
Citations
4196
Access
Closed

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Cite This

Carlos A. Coello Coello, Gregorio Toscano‐Pulido, M.S. Lechuga (2004). Handling multiple objectives with particle swarm optimization. IEEE Transactions on Evolutionary Computation , 8 (3) , 256-279. https://doi.org/10.1109/tevc.2004.826067

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DOI
10.1109/tevc.2004.826067

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