Title: Control scaling factor of cuckoo search algorithm using learning automata

Authors: Yaohua Lin; Lijin Wang; Yiwen Zhong; Cuiping Zhang

Addresses: 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 ' College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou, China ' College of Management, Fujian University of Traditional Chinese Medicine, Fuzhou, China

Abstract: In this study, we seek an optimal scaling factor of cuckoo search algorithm by using learning automata. In the presented method, the same learning automaton is built for each individual, and a set of actions of each learning automaton are set to several constant scaling factors. Moreover, the linear reward-penalty learning algorithm is used in learning automaton to select the optimal scaling factor of each individual. Extensive experiments on 20 benchmark functions demonstrate better effectiveness and efficiency of controlling scaling factor of cuckoo search by using learning automata.

Keywords: cuckoo search; learning automata; scaling factor; linear reward-penalty.

DOI: 10.1504/IJCSM.2016.080088

International Journal of Computing Science and Mathematics, 2016 Vol.7 No.5, pp.476 - 484

Received: 26 May 2016
Accepted: 08 Aug 2016
Published online: 01 Nov 2016 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article