Title: Enhancing cuckoo search algorithm with complement strategy

Authors: Hu Peng; Hua Lu; Changshou Deng

Addresses: School of Computer and Big Data Science, Jiujiang University, Jiujiang 332005, China ' School of Computer and Big Data Science, Jiujiang University, Jiujiang 332005, China ' School of Computer and Big Data Science, Jiujiang University, Jiujiang 332005, China

Abstract: Cuckoo search (CS) algorithm is proposed by Yang and Deb, which is a simple and effective swarm intelligence algorithm. In each iteration, the CS search for new solutions by the global explorative random walk and the local random walk, the greedy strategy is used to choose a better solution. However, the CS uses a single strategy and fixed parameters, in which performance of the CS in balancing exploitation and exploration is inadequate, which results in poor convergence performance of the CS. To cope with this problem, a novel CS variant with complement strategy (CoCS) was proposed by us, in which the new solution is generated by two strategies in a random manner. One of the strategy is an improved Lêvy flights, and the other is adaptive to determine the step size according to the fitness value of the step size and the number of current iterations. The algorithm also uses an improved random walk. The proposed CoCS, the standard CS, and other excellent CS variants were tested on 28 benchmark functions of CEC 2013 test suite. Experimental results prove that the CoCS is superior to these competitors.

Keywords: cuckoo search algorithm; global optimisation; complement strategy; dynamic parameter adjustment.

DOI: 10.1504/IJICA.2022.128439

International Journal of Innovative Computing and Applications, 2022 Vol.13 No.5/6, pp.314 - 324

Received: 28 Aug 2020
Accepted: 18 Jan 2021
Published online: 23 Jan 2023 *

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