Recent advances in Multi-objective Cuckoo Search Algorithm, its variants and applications

• Published in: Archives of computational methods in engineering Vol. 32; no. 5; pp. 3213 - 3240
• Main Authors: Makhadmeh, Sharif Naser, Awadallah, Mohammed A., Kassaymeh, Sofian, Al-Betar, Mohammed Azmi, Sanjalawe, Yousef, Kouka, Shaimaa, Al-Redhaei, Anessa
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Nature B.V 01.06.2025
• Subjects: Algorithms; Breeding; Multiple objective analysis; Optimization; Search algorithms
• ISSN: 1134-3060; 1886-1784
• DOI: 10.1007/s11831-025-10240-9

The Cuckoo Search Algorithm (CSA) is an optimization algorithm inspired by the brood parasitism behavior of cuckoo birds. It mimics the reproductive and breeding tactics of cuckoos to tackle optimization challenges. To better handle multi-objective optimization problems (MOPs), a variation called the multi-objective CSA (MOCSA) has been developed. MOCSA is designed to uncover a spectrum of solutions, each providing a balance between various objectives, thereby allowing decision-makers to choose the optimal solution according to their specific preferences. The literature has witnessed a notable increase in the number of published MOCSAs, with MOCSA research papers recorded in the SCOPUS database. This paper presents a comprehensive survey of 123 distinct variants of MOCSAs published in scientific journals. Through this survey, researchers will gain insights into the growth of MOCSA, the theoretical foundations of multi-objective optimization and the MOCSA algorithm, the various existing MOCSA variants documented in the literature, the application domains in which MOCSA has been employed, and a critical analysis of its performance. In sum, this paper provides future research directions for MOCSA. Overall, this survey provides a valuable resource for researchers seeking to explore and understand the advancements, applications, and potential future developments in the field of multi-objective CSA.