Strength pareto particle swarm optimization and hybrid ea-pso for multi-objective optimization

Authors:
Ahmed Elhossini;Shawki Areibi;Robert Dony
Affiliations:
School of Engineering, University of Guelph, Guelph, ON, N1G 2W1, Canada. aelhossi@uoguelph.ca;School of Engineering, University of Guelph, Guelph, ON, N1G 2W1, Canada. sareibi@uoguelph.ca;School of Engineering, University of Guelph, Guelph, ON, N1G 2W1, Canada. rdony@uoguelph.ca
Venue:
Evolutionary Computation
Year:
2010

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Abstract

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.