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Multi-objective particle swarm optimization algorithm for engineering constrained optimization problems

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3 Author(s)
Dekun Tan ; Department of Computer Science & Technology, Nanchang Institute of Technology, Jiangxi Province, China 330099 ; Wenhai Luo ; Qing Liu

This paper proposes a modified particle swarm optimization algorithm for engineering optimization problems with constraints, in which the penalty function is employed to the traditional PSO algorithm, and at the same time adjusts the personal optimum and global optimum to make PSO being able to solve the non-linear programming problems, then the multi-objective problem can be converted into single objective problem. Moreover, the constraint term played its role in the process of generating particles, those pariticles which don't meet the constraint condition are eliminated. The actual engineering design optimization problem is tested and the results show that the multi-objective particle swarm optimization algorithm can be used to solve the multi-objective constrained optimization problem. Comparison with Genetic Algorithm confirms that the proposed algorithm can find better solutions, and converge quickly.

Published in:

Granular Computing, 2009, GRC '09. IEEE International Conference on

Date of Conference:

17-19 Aug. 2009