A new multi-swarm multi-objective particle swarm optimization based on pareto front set

Authors:
Zenghui Wang
Affiliations:
School of Engineering, University of South Africa, Pretoria, South Africa
Venue:
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence
Year:
2011

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Abstract

In this paper, a new multi-swarm method is proposed for multi-objective particle swarm optimization. To enhance the Pareto front searching ability of PSO, the particles are divided into many swarms. Several swarms are dynamically searching the objective space around some points of the Pareto front set. The rest of particles are searching the space keeping away from the Pareto front to improve the global search ability. Simulation results and comparisons with existing Multi-objective Particle Swarm Optimization methods demonstrate that the proposed method effectively enhances the search efficiency and improves the search quality.