Title: An improved adaptive cuckoo search algorithm based on the population feature and iteration information

Authors: Jia Chaochuan; Yang Ting; Wang Chuanjiang; Fan Binghui; He Fugui

Addresses: College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao, Shandong, 266510, China; College of Electronics and Information Engineering, West Anhui University, Lu'an, Anhui, 237012, China ' College of Electronics and Information Engineering, West Anhui University, Lu'an, Anhui, 237012, China ' College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao, Shandong, 266510, China ' College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao, Shandong, 266510, China ' College of Electronics and Information Engineering, West Anhui University, Lu'an, Anhui, 237012, China

Abstract: Cuckoo search (CS) is widely used to solve many optimisation problem, which is a biologically inspired the brood parasitic behaviour of a type of cuckoos and the Lévy flights behaviour of some animals. However, it has been demonstrated to easily get trapped into local optimal solutions and slow convergence speed. Therefore, an improved adaptive cuckoo search (IACS) optimisation algorithm is proposed in this article. Two adaptive strategies based on the population feature and iteration information feedback which are integrated into the CS algorithm to adjust the parameters p_a and α₀. We compared the proposed algorithm to CS and five variants on the 30 benchmark functions proposed in CEC 2014. In addition, the proposed algorithm and CS are integrated into support vector machine (SVM) for classification. Experimental results certify that the modified algorithm is superior to the CS for most optimisation problems and has better performance than the other variants of CS algorithm.

Keywords: cuckoo search; optimisation algorithm; adaptive strategy; support vector machine; SVM; classification.

DOI: 10.1504/IJCNDS.2020.106289

International Journal of Communication Networks and Distributed Systems, 2020 Vol.24 No.3, pp.233 - 248

Received: 05 Sep 2018
Accepted: 24 Oct 2018
Published online: 02 Apr 2020 *

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