Title: Enhanced chicken swarm optimisation for function optimisation problem

Authors: Min Lin; Yiwen Zhong; Juan Lin; Xiaoyu Lin

Addresses: College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou, China; Key Laboratory of Smart Agriculture and Forestry (Fujian Agriculture and Forestry University), Fuzhou 350002, China ' College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou, China; Key Laboratory of Smart Agriculture and Forestry (Fujian Agriculture and Forestry University), Fuzhou 350002, China ' College of Computer and information Science, Fujian Agriculture and Forestry University, Fuzhou, China ' College of Computer and information Science, Fujian Agriculture and Forestry University, Fuzhou, China

Abstract: Chicken Swarm Optimisation (CSO) algorithm is a novel swarm intelligence algorithm. Improper balance between the diversification and intensification may degrade its performance. In order to tackle this problem, this paper proposes an enhanced CSO (ECSO) algorithm which can get better balance between diversification and intensification for the swarm. Specifically, a novel adaptive neighbourhood strategy is used by the location update equation of roosters, so roosters can focus on exploration in early stage and on exploitation in late stage. In addition, learning from chicks is introduced into the location update equation of hens, so that hens can learn from chicks occasionally and increase the diversity of swarm. Experiments on sixteen benchmark problems were conducted to compare the proposed ECSO algorithm with the original CSO algorithm and other classical swarm intelligent algorithms. The results show that ECSO algorithm can achieve good optimisation results in terms of both optimisation accuracy and robustness.

Keywords: chicken swarm optimisation; adaptive neighbourhood; learning from chicks; function optimisation; swarm intelligence.

DOI: 10.1504/IJWMC.2018.096009

International Journal of Wireless and Mobile Computing, 2018 Vol.15 No.3, pp.258 - 269

Received: 28 Feb 2018
Accepted: 06 Aug 2018
Published online: 06 Nov 2018 *

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