Title: Constrained solution of CEC 2017 with monarch butterfly optimisation

Authors: Hui Hu; Zhaoquan Cai; Song Hu; Yingxue Cai; Jia Chen; Sibo Huang

Addresses: Department of Information Science and Technology, Huizhou University, 516007, Huizhou Guangdong, China ' Department of Information Science and Technology, Huizhou University, 516007, Huizhou Guangdong, China ' Educational Technology Centre, Huizhou University, 516007, Huizhou Guangdong, China ' Educational Technology Centre, Huizhou University, 516007, Huizhou Guangdong, China ' Educational Technology Centre, Huizhou University, 516007, Huizhou Guangdong, China ' Educational Technology Centre, Huizhou University, 516007, Huizhou Guangdong, China

Abstract: Recently, inspired by the behaviour of monarch butterfly in North America, Wang et al. proposed a new kind of swarm intelligence algorithm, called Monarch Butterfly Optimisation (MBO). Since it was proposed, it has been widely studied and applied in various engineering fields. In this paper, we apply MBO algorithm to solve CEC 2017 competition on constrained real-parameter optimisation. Also, the performance of MBO on 21 constrained CEC 2017 real-parameter optimisation problems is compared with five other state-of-the-art evolutionary algorithms. The experimental results indicate that MBO algorithm performs much better than other five evolutionary algorithms on most cases. It is strongly proven that MBO is a very promising algorithm for solving constrained engineering problems.

Keywords: MBO; monarch butterfly optimisation; migration operator; butterfly adjusting operator; constrained optimisation.

DOI: 10.1504/IJWMC.2019.099022

International Journal of Wireless and Mobile Computing, 2019 Vol.16 No.2, pp.138 - 145

Received: 19 May 2018
Accepted: 18 Sep 2018
Published online: 12 Apr 2019 *

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