A novel particle swarm optimization for constrained optimization problems

Authors:
Xiangyong Li;Peng Tian;Min Kong
Affiliations:
Antai School of Management, Shanghai Jiaotong University, Shanghai, China;Antai School of Management, Shanghai Jiaotong University, Shanghai, China;Antai School of Management, Shanghai Jiaotong University, Shanghai, China
Venue:
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Year:
2005

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Abstract

This paper proposes a novel particle swarm optimization (PSO) for solving constrained optimization problems. Based upon the acceptable assumption that any feasible solution is better than any infeasible solution, a new mechanism for constraints handling is incorporated in the standard particle swarm optimization. In addition to the mechanism of constraints handling, a mutation strategy to increase population diversity is added to the proposed algorithm to improve convergence. Experimental results compared with genetic algorithm and a standard PSO show that the proposed algorithm is a desirable and competitive algorithm for solving constrained optimization problems.