Chaotic Multi-Objective Particle Swarm Optimization Algorithm Incorporating Clone Immunity

Ying Sun, Yuelin Gao and Xudong Shi
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Ying Sun: School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, China
Yuelin Gao: Ningxia Province Key Laboratory of Intelligent Information and Data Processing, North Minzu University, Yinchuan 750021, China
Xudong Shi: School of Mathematics and Statistics, Ningxia University, Yinchuan 750021, China

Mathematics, 2019, vol. 7, issue 2, 1-16

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.

Keywords: multi-objective optimal problem; particle swarm optimization; clone immunity; chaotic sequence (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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