Cuckoo search: recent advances and applications

By Xin-She Yang, Suash Deb. Neural computing & applications (Print), 2013

1,049 citations recorded by the source corpus.

Cuckoo search (CS) is a relatively new algorithm, developed by Yang and Deb in 2009, and the same has been found to be efficient in solving global optimization problems. In this paper, we review the fundamental ideas of cuckoo search and the latest developments as well as its applications. We analyze the algorithm and gain insight into its search mechanisms and find out why it is efficient. We also discuss the essence of algorithms and its link to self-organizing systems, and finally, we propose some important topics for further research.

Subfields: Artificial Intelligence, Biomedical Engineering, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/491bcb0edab5f8239c3e069f1268449d85739c7c

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Citations in the corpus

Metaheuristic research: a comprehensive survey (2018, methodology): “Table 4 Acronyms of metaheuristics abbreviations used in this study Abbreviations Acronyms AAA Alienated Ant Algorithm (Uymaz et al. 2015) AAA2 Artificial Algae Algorithm (Bandieramonte et al. 2010) ABC Artificial Bee Colony (Karaboga 2005) ABO African Buffalo Optimization (Odili and Mohmad Kahar 20”
A survey on new generation metaheuristic algorithms (2019, background): “Gandomi and Yang (2014) propose a chaotic BA to increase the global search capacity of BA for robust optimization.”
Bio inspired computing - A review of algorithms and scope of applications (2016, methodology): “Cuckoo search (Kar, 2014; Yang & Deb, 2014; Walia and Kapoor, 2014; Bhandari et al., 2014; Araghi et al., 2014; Kumar and Rawat, 2015; Gotmare et al., 2015) has been used for multicriteria heuristic search problems, multi-objective heuristic problems, maximization of multiple opposite objectives,…”
Nature-Inspired Optimization Algorithms: Challenges and Open Problems (2020, methodology): “…algorithms such as gradient-based algorithms, the interior-point method and trust-region method, most of such algorithms are gradient-based and local search algorithms [Boyd and Vandenberghe(2004), Yang(2014)], which means that the ﬁnal solutions may depend on the initial starting points.”
Elephant Herding Optimization (2015, methodology): “Inspired by this phenomenon, ACO [10, 11] was proposed by Dorigo et al. Recently, more effective swarm intelligence algorithms have been proposed, such as artificial bee colony (ABC) [12, 13], cuckoo search (CS) [14-20], bat algorithm (BA) [21-24], firefly algorithm (FA) [25-28], animal migration…”
A new bio-inspired optimisation algorithm: Bird Swarm Algorithm (2016, background): “PSO (Rezaee Jordehi & Jasni, 2013), Ant Colony Optimization (Dorigo & Stutzle, 2004), Artiﬁcial Fish Swarm Algorithm (Gao, Wu, Zenger, & Huang, 2010), Artiﬁcial Bee Colony (Karaboga & Basturk, 2007), Glowworm Swarm Optimization (Krishnanand & Ghose, 2009), Cuckoo Search (Yang & Deb, 2014), Bat Algor”