Multi-objective particle swarm optimization based on minimal particle angle

Authors:
Dun-Wei Gong;Yong Zhang;Jian-Hua Zhang
Affiliations:
School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, Jiangsu, P. R. China;School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, Jiangsu, P. R. China;School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, Jiangsu, P. R. China
Venue:
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Year:
2005

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Abstract

Particle swarm optimization is a computational intelligence method of solving the multiobjective optimization problems. But for a given particle, there is no effective way to select its globally optimal particle and locally optimal particle. The particle angle is defined by the particle's objective vector. The globally optimal particle is selected according to the minimal particle angle. Updating the locally optimal particle and particle swarm is based on the Pareto dominance relationship between the locally optimal particle and the offspring particles and the particle's density. A multiobjective particle swarm optimization based on the minimal particle angle is proposed. The algorithm proposed is compared with sigma method ,NSPSO method and NSGA-II method on four complicated benchmark multiobjective function optimization problems. It is shown from the results that the Pareto front obtained with the algorithm proposed in this paper has good distribution, approach and extension properties.