Correlative particle swarm optimization for multi-objective problems

Authors:
Yuanxia Shen;Guoyin Wang;Qun Liu
Affiliations:
Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China and Anhui University of Technology, Maanshan, China;Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China;Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China
Venue:
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part II
Year:
2011

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Abstract

Particle swarm optimization (PSO) has been applied to multiobjective problems. However, PSO may easily get trapped in the local optima when solving complex problems. In order to improve convergence and diversity of solutions, a correlative particle swarm optimization (CPSO) with disturbance operation is proposed, named MO-CPSO, for dealing with multi-objective problems. MO-CPSO adopts the correlative processing strategy to maintain population diversity, and introduces a disturbance operation to the nondominated particles for improving convergence accuracy of solutions. Experiments were conducted on multi-objective benchmark problems. The experimental results showed that MO-CPSO operates better in convergence metric and diversity metric than three other related works.