NRS-CSO: neighbourhood rough set-based cat swarm optimisation algorithms. (4th April 2021)

• Record Type: Journal Article
• Authors: Leng, Zi-Hao; Fan, Jian-Cong
• Is Part Of: International journal of computing science and mathematics. Volume 13:Number 2(2021)
• Page Start: 156
• Page End: 166
• Publication Date: 2021-04-04
• Subjects: cat swarm optimisation -- neighbourhood rough set -- computational intelligence -- function optimisation; Mathematics -- Periodicals; Computer science -- Periodicals; Mathematics -- Data processing -- Periodicals; 510.285
• Languages: English
• ISSNs: 1752-5055

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.