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

Affiliated Institutions

Related Publications

Handbook of Genetic Algorithms

This book sets out to explain what genetic algorithms are and how they can be used to solve real-world problems. The first objective is tackled by the editor, Lawrence Davis. Th...

1991 7308 citations

Publication Info

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

Social Impact

Social media, news, blog, policy document mentions

Citation Metrics

4196
OpenAlex
401
Influential
3697
CrossRef

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

Identifiers

DOI
10.1109/tevc.2004.826067

Data Quality

Data completeness: 77%