An analysis of why cuckoo search does not bring any novel ideas to optimization. (June 2022)

• Record Type: Journal Article
• Authors: Camacho-Villalón, Christian L.; Dorigo, Marco; Stützle, Thomas
• Is Part Of: Computers & operations research. Volume 142(2022)
• Publication Date: 2022-06
• Subjects: Evolution strategies -- Differential evolution -- Continuous optimization -- Metaheuristic algorithm; Operations research -- Periodicals; Electronic digital computers -- Periodicals; 004.05
• DOI: 10.1016/j.cor.2022.105747
• Languages: English
• ISSNs: 0305-0548

Abstract

Abstract: It has been more than 10 years since the first version of cuckoo search was proposed by Yang and Deb and published in the proceedings of the World Congress on Nature & Biologically Inspired Computing, in 2009. The two main articles on cuckoo search have now been cited almost 8 700 times (according to Google scholar ), there are books and chapters published about this algorithm, and a special issue has been organized to celebrate its first decade of existence. Given the popularity of the algorithm and its widespread use, it is quite surprising that no one has ever formally investigated its obvious resemblance to evolutionary algorithms. In this article, we conduct a rigorous analysis of cuckoo search in which we identify the concepts used in the algorithm and show that these are the exact same concepts proposed in the ( μ + λ )–evolution strategy, a well-known evolutionary algorithm introduced originally in 1981, and in the classic differential evolution algorithm introduced in 1997. We analyze the "cuckoos' parasitic behavior" metaphor that inspired the algorithm according to three criteria—usefulness, novelty and sound motivation—that allow to clarify whether the use of the metaphor is justified or not. The result is that cuckoo search does not comply with any of these criteria. Surprisingly, we found that the algorithm the authors proposed for cuckoo search does not match the publicly available implementation they provided; moreover, neither of them really follows the metaphor of the cuckoos that inspired the algorithm. Highlights: A detailed, component-based analysis of the popular cuckoo search (CS) is presented. CS uses concepts that have been in the evolutionary computation literature for years. The metaphor of cuckoos' reproduction does not contain useful ideas for optimization. The behavior that inspired CS, the algorithm and its implementation are inconsistent.