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Mathematics 2019, 7(2), 146; https://doi.org/10.3390/math7020146

Chaotic Multi-Objective Particle Swarm Optimization Algorithm Incorporating Clone Immunity

1
School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, China
2
Ningxia Province Key Laboratory of Intelligent Information and Data Processing, North Minzu University, Yinchuan 750021, China
3
School of Mathematics and Statistics, Ningxia University, Yinchuan 750021, China
*
Author to whom correspondence should be addressed.
Received: 22 November 2018 / Revised: 22 January 2019 / Accepted: 28 January 2019 / Published: 3 February 2019
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Abstract

It is generally known that the balance between convergence and diversity is a key issue for solving multi-objective optimization problems. Thus, a chaotic multi-objective particle swarm optimization approach incorporating clone immunity (CICMOPSO) is proposed in this paper. First, points in a non-dominated solution set are mapped to a parallel-cell coordinate system. Then, the status of the particles is evaluated by the Pareto entropy and difference entropy. At the same time, the algorithm parameters are adjusted by feedback information. At the late stage of the algorithm, the local-search ability of the particle swarm still needs to be improved. Logistic mapping and the neighboring immune operator are used to maintain and change the external archive. Experimental test results show that the convergence and diversity of the algorithm are improved. View Full-Text
Keywords: multi-objective optimal problem; particle swarm optimization; clone immunity; chaotic sequence multi-objective optimal problem; particle swarm optimization; clone immunity; chaotic sequence
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Sun, Y.; Gao, Y.; Shi, X. Chaotic Multi-Objective Particle Swarm Optimization Algorithm Incorporating Clone Immunity. Mathematics 2019, 7, 146.

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