Title: A modified extended particle swarm optimisation algorithm to solve the directing orbits of chaotic systems

Authors: Simin Mo; Jianchao Zeng; Weibin Xu; Chaoli Sun

Addresses: Division of Industrial and System Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China; Institute of Economics and Management, Taiyuan University of Science and Technology, Taiyuan 030024, China ' Division of Industrial and System Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China; Institute of Economics and Management, Taiyuan University of Science and Technology, Taiyuan 030024, China ' Institute of Economics and Management, Taiyuan University of Science and Technology, Taiyuan 030024, China ' State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, China

Abstract: In order to solve the problem of the poor local search capability of the extended particle swarm optimisation (EPSO) algorithm, the modify extended particle swarm optimisation algorithm (MEPSO) was proposed, which reduces magnitude of total forces exerting on each particle through decreasing the number of each particle effected by other particles. Meanwhile, the number of the particles removed is analysed theoretically. And it was proved that MEPSO can converge to the global optimum with the probability 1. Compared with the related algorithms, the presented algorithm can effectively balance the global and local search and improve optimisation performances. Finally, MEPSO can better solve the problem of directing orbits of chaotic systems.

Keywords: extended particle swarm optimisation algorithm; particle total forces; global search; local search.

DOI: 10.1504/IJICT.2019.096596

International Journal of Information and Communication Technology, 2019 Vol.14 No.1, pp.16 - 30

Received: 27 Jan 2016
Accepted: 08 Mar 2016
Published online: 07 Dec 2018 *

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