Multiobjective Constriction Particle Swarm Optimization and Its Performance Evaluation

Authors:
Yifeng Niu;Lincheng Shen
Affiliations:
College of Mechatronic Engineering and Automation, National University of Defense Technology, 410073, Changsha, China;College of Mechatronic Engineering and Automation, National University of Defense Technology, 410073, Changsha, China
Venue:
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Year:
2009

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Abstract

A novel multiobjective constriction particle swarm optimization (MOCPSO) is presented. MOCPSO not only uses mutation operator to avoid earlier convergence and uses adaptive weight to raise the search capacity, but also uses a new crowding operator to improve the distribution of nondominated solutions along the Pareto front, and uses the uniform design to obtain the optimal parameter combination. The sound evaluation criteria for multiobjective optimization algorithm are given, and some typical test functions are introduced. Experimental results show that MOCPSO has faster convergent speed and better search capacity than other multiobjective particle swarm optimization algorithms, especially when there are more than two objectives.