A survey on applications and variants of the cuckoo search algorithm

• Published in: Applied soft computing 2017-12, Vol.61, p.1041-1059
• Main Authors: Shehab, Mohammad, Khader, Ahamad Tajudin, Al-Betar, Mohammed Azmi
• Format: Article
• Language: English
• Subjects: Cuckoo search algorithm; Metaheuristic; Nature-inspired algorithms; Optimization; Swarm-based approach
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2017.02.034

[Display omitted] •We introduce a literature review considering articles on cuckoo search algorithm.•Around 150 suitable articles are identified and classified according to defined methodology.•We focus on the growth, variants, applications and modifications of the cuckoo search algorithm.•We mention on the possible ways to use the cuckoo search in the future work. This paper introduces a comprehensive and exhaustive overview of the cuckoo search algorithm (CSA). CSA is a metaheuristic swarm-based approach established by Yang and Deb [10] to emulate the cuckoo breeding behavior. Owing to the successful application of CSA for a wide variety of optimization problems, since then, researchers have developed several new algorithms in this field. This article displays a comprehensive review of all conducting intensive research survey into the pros and cons, main architecture, and extended versions of this algorithm. It is worth mentioning that the materials of this survey paper are categorized in accordance with the structure of the CSA in which the materials are divided into the CSA versions and modification, publication years, the CSA applications areas, and the hybridization of CSA. The survey paper ends with solid conclusions about the current research on CSA and the possible future directions for the relevant audience and readers. The researchers and practitioners on CSA belong to a wide range of audiences from the domains of optimization, engineering, medical, data mining, clustering, etc., who will benefit from this study.