An improved particle swarm pareto optimizer with local search and clustering

Authors:
Ching-Shih Tsou;Hsiao-Hua Fang;Hsu-Hwa Chang;Chia-Hung Kao
Affiliations:
Department of Business Administration, National Taipei College of Business, Taipei, Taiwan;Department of Information Management, Shih Hsin University, Taipei, Taiwan;Department of Business Administration, National Taipei College of Business, Taipei, Taiwan;Department of Information Management, Shih Hsin University, Taipei, Taiwan
Venue:
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
Year:
2006

Citing 8
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Abstract

In this paper, the local search and clustering mechanism are incorporated into the Multi-Objective Particle Swarm Optimization (MOPSO). The local search mechanism prevents premature convergence, hence enhances the convergence of optimizer to true Pareto-optimal front. The clustering mechanism reduces the nondominated solutions to a handful number such that we can speed up the search and maintain the diversity of the nondominated solutions. The performance of this approach is evaluated on metrics from literature. The results against a three objectives optimization problem show that the proposed Pareto optimizer is competitive with the strength Pareto evolutionary algorithm (SPEA) in converging towards the front and generates a well-distributed nondominated set.