Multi-objective adaptive chaotic particle swarm optimization algorithm

被引：0
作者：
Yang, Jing-Ming [1 ]
Ma, Ming-Ming [1 ]
Che, Hai-Jun [1 ]
Xu, De-Shu [1 ]
Guo, Qiu-Chen [1 ]
机构：
[1] Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao
来源：
Kongzhi yu Juece/Control and Decision | 2015年 / 30卷 / 12期
关键词：
Adaptive mutation; Chaotic Logistic map; Crowding distance; Multi-objective optimization; Particle swarm;
D O I：
10.13195/j.kzyjc.2014.1869
中图分类号：
学科分类号：
摘要：
A multi-objective adaptive chaotic particle swarm optimization (MACPSO) algorithmis proposed. Firstly, on the basis of the chaotic sequence, a new dynamic weighting method is proposed to select the global optimum particle. Then, the calculation method of crowding distance in NSGA-II is improved and applied to a rigorous external archive updating strategy. Finally, an adaptive mutation strategy based on the generational distance is presented for the external archive. The operations above mentioned not only enhance the convergence performance of the proposed algorithm, but also improve the uniformity of the Pareto optimal solution. The experimental results show the effectiveness of the proposed method. © 2015, Northeast University. All right reserved.
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页码：2168 / 2174
页数：6
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