Strength Pareto particle swarm optimization and hybrid EA-PSO for multi-objective optimization

This paper proposes an efficient particle swarm optimization (PSO) technique that can handle multi-objective optimization problems. It is based on the strength Pareto approach originally used in evolutionary algorithms (EA). The proposed modified particle swarm algorithm is used to build three hybri...

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Veröffentlicht in:Evolutionary computation Jg. 18; H. 1; S. 127
Hauptverfasser: Elhossini, Ahmed, Areibi, Shawki, Dony, Robert
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States 01.03.2010
Schlagworte:
Algorithms
Computer Simulation
Models, Theoretical
Search Engine - methods
ISSN:1530-9304, 1530-9304
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Zusammenfassung:This paper proposes an efficient particle swarm optimization (PSO) technique that can handle multi-objective optimization problems. It is based on the strength Pareto approach originally used in evolutionary algorithms (EA). The proposed modified particle swarm algorithm is used to build three hybrid EA-PSO algorithms to solve different multi-objective optimization problems. This algorithm and its hybrid forms are tested using seven benchmarks from the literature and the results are compared to the strength Pareto evolutionary algorithm (SPEA2) and a competitive multi-objective PSO using several metrics. The proposed algorithm shows a slower convergence, compared to the other algorithms, but requires less CPU time. Combining PSO and evolutionary algorithms leads to superior hybrid algorithms that outperform SPEA2, the competitive multi-objective PSO (MO-PSO), and the proposed strength Pareto PSO based on different metrics.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1530-9304
1530-9304
DOI:10.1162/evco.2010.18.1.18105