An Improved Particle Swarm Optimization with Feasibility-Based Rules for Constrained Optimization Problems

Authors:
Chao-Li Sun;Jian-Chao Zeng;Jeng-Shyang Pan
Affiliations:
Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, Taiyuan, P.R. China 030024;Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, Taiyuan, P.R. China 030024;Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan 807
Venue:
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
Year:
2009

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Abstract

This paper presents an improved particle swarm optimization (IPSO) to solve constrained optimization problems, which handles constraints based on certain feasibility-based rules. A turbulence operator is incorporated into IPSO algorithm to overcome the premature convergence. At the same time, a set called FPS is proposed to save those P best locating in the feasible region. Different from the standard PSO, g best in IPSO is chosen from the FPS instead of the swarm. Furthermore, the mutation operation is applied to the P best with the maximal constraint violation value in the swarm, which can guide particles to close the feasible region quickly. The performance of IPSO algorithm is tested on a well-known benchmark suite and the experimental results show that the proposed approach is highly competitive, effective and efficient.