An improved cuckoo search algorithm using elite opposition-based learning and golden sine operator

Abstract

An improved cuckoo search algorithm using elite opposition-based learning and golden sine operator Peng ChengLi, Xuan YuZhang, AzlanMohd. Zain & Kai QingZhou The existing cuckoo search (CS) algorithm has the drawbacks of slow convergence speed, low convergence accuracy, and easy to fall into local optimum. An improved cuckoo search algorithm is proposed in this manuscript to overcome the mentioned shortages using elite opposition-based learning and golden sine operator (EOBL-GS-CS). The modifications could be summarized from two aspects. On the one hand, the elite opposition-based learning (EOBL) mechanism is employed to improve the diversity and quality of the population, preventing the algorithm from falling into the local optimum. On the other hand, the golden sine operator accelerates the algorithm’s convergence speed and improves the algorithm's optimization ability. In the verification part, 14 unimodal and multimodal benchmark functions are used to highlight the characteristics of the proposed algorithm. The experimental results show that, compared with the standard CS and other variants, the EOBL-GS-CS has a faster convergence speed, higher solution accuracy, and significantly improved optimization performance.