A Multi-Objective Particle Swarm Optimization Algorithm Integrating Multiply Strategies
XIE Cheng-wang1,2, ZOU Xiu-fen1, XIA Xue-wen2, WANG Zhi-jie2
1. School of Mathematics and Statistics, Wuhan University, Wuhan, Hubei 430072, China;
2. School of Software, East China Jiaotong University, Nanchang, Jiangxi 330013, China
In order to improve the overall performance of multi-objective particle swarm optimization algorithm (MOPSO) in solving complicated multi-objective optimization problems, a multi-objective particle swarm optimization algorithm integrating multiply strategies (MSMOPSO) was proposed in the paper.A new initialization approach of combining uniformization and randomization was adopted in the MSMOPSO.Secondly, a disturbance item was added to the particle's velocity updating formula.Thirdly, a simplified k-nearest neighbor approach was applied to preserve the diversity of external archive.Finally, every non-dominated particle in the external archive was assigned the property of lifespan and the lifespan value would be adjusted dynamically during the run of the MSMOPSO.The experimental results illustrate that the proposed algorithm significantly outperforms the other five peer competitors in terms of GD, SP on ZDT and DTLZ test instances set.
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2015, Vol. 43 
