Improved chaos multi-objective particle swarm optimization

被引：0

作者：

Zhang X. ^{[1
]}

Wang X. ^{[1
]}

Niu Y. ^{[1
]}

Cui G. ^{[1
]}

机构：

[1] College of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, Hanan

来源：

Journal of Computational and Theoretical Nanoscience | 2016年 / 13卷 / 06期

基金：

中国国家自然科学基金;

关键词：

Chaotic maps; Efficient non-dominated sorting; Multi-objective optimization; Particle swarm optimization;

D O I：

10.1166/jctn.2016.5197

中图分类号：

学科分类号：

摘要：

Evolutionary algorithms have been effectively used to solve multi-objective optimization problems. However, considering the problems of slow convergence speed and easily jumping into local optimum of evolutionary optimization algorithms in finding the best solution, a chaos multi-objective particle swarm optimization using efficient non-dominated sort (CMOPSO-ENS) is proposed. Efficient non-dominated sorting (ENS) is one of the main highlights in this algorithm, in which it greatly improves runtime. What's more, the chaotic maps are used to optimize local best solution in the algorithm. Several benchmark functions are applied to test the search capability of the improved algorithm. The simulation results demonstrate that the proposed CMOPSO-ENS algorithm surpasses existing MOPSO and NSGA-II algorithms in both convergence and diversity, especially in solving problems with complex high-dimensional functions. Copyright © 2016 American Scientific Publishers. All rights reserved.

引用

页码：3659 / 3666

页数：7

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