Paper Info
Title
An Improved Cuckoo Search Algorithm Using Elite Opposition-Based Learning and Golden Sine Operator
Abstract
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.
Year
DOI
Venue
2022
10.1007/978-3-031-06794-5_23
Artificial Intelligence and Security
Keywords
DocType
ISSN
Cuckoo search, Elite opposition-based learning, Golden sine operator, Function optimization, Modification
Conference
0302-9743
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Li Peng-Cheng100.34
Zhang Xuan-Yu200.34
Zain Azlan Mohd300.68
Zhou Kai-Qing400.34