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| Pareto Archive MultiObjective Particle Swarm Optimization |
| LEI DeMing1,2, WU ZhiMing2 |
1.School of Automation, Wuhan University of Technology, Wuhan 430070
2.Institute of Automation, Shanghai Jiaotong University, Shanghai 200030 |
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Abstract Pareto archive multiobjective particle swarm optimization (PAMOPSO) is designed, in which the improved method of strength Pareto evolutionary algorithm 2 (SPEA2) is used to maintain external archive and the global best location for each particle is selected in the procedure of archive maintenance. PAMOPSO is applied to five test instances and compared with other three multiobjective optimization algorithms. The computational results demonstrate that PAMOPSO has good performance in multiobjective optimization.
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Received: 27 December 2004
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2006, Vol. 19 
