Title: NRS-CSO: neighbourhood rough set-based cat swarm optimisation algorithms

Authors: Zi-Hao Leng; Jian-Cong Fan

Addresses: College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China; Provincial Key Laboratory for Information Technology of Wisdom Mining of Shandong Province, Shandong University of Science and Technology, Qingdao, China ' College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China; Provincial Key Laboratory for Information Technology of Wisdom Mining of Shandong Province, Shandong University of Science and Technology, Qingdao, China; Provincial Experimental Teaching Demonstration Center of Computer, Shandong University of Science and Technology, Qingdao, China

Abstract: Cat swarm optimisation (CSO) is a typical evolutionary method inspired by the cats in the nature for solving optimisation problem. After CSO is first proposed, it has been improved and applied in different fields, the series of CSO algorithms has been verified that they have better performance compared to many other swarm optimisation algorithms. In this research, we proposed a novel improved CSO named neighbourhood rough set-based cat swarm optimisation (NRS-CSO) and use neighbourhood rough set theory to improve the CSO algorithm. The NRS-CSO presented in this paper is implemented on a number of benchmark optimisation problems. The optimisation results are compared with four different optimisation algorithm including PSO and different variants of CSO. Experimental results show that in compare with the other algorithms, the proposed algorithm improves the performance of its final solution, it can take less time to converge and the whole iteration is less.

Keywords: cat swarm optimisation; neighbourhood rough set; computational intelligence; function optimisation.

DOI: 10.1504/IJCSM.2021.114178

International Journal of Computing Science and Mathematics, 2021 Vol.13 No.2, pp.156 - 166

Received: 04 Dec 2018
Accepted: 16 Mar 2019
Published online: 13 Apr 2021 *

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