[1]WU Yiquan,ZHOU Jianwei.Overview of the cuckoo search algorithm and its applications[J].CAAI Transactions on Intelligent Systems,2020,15(3):435-444.[doi:10.11992/tis.201811005]
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Overview of the cuckoo search algorithm and its applications

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 15 Number of periods: 2020 3 Page number: 435-444 Column: 综述 Public date: 2020-05-05
Title:
Overview of the cuckoo search algorithm and its applications
Author(s):
WU Yiquan1; 2; 3;  ZHOU Jianwei1
1. College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China;
2. Beijing Key Laboratory of Urban Spatial Information Engineering, Beijing Institute of Surveying and Mapping, Beijing 100038, China;
3. State Key Laboratory of Digital Publishing Technology, Peking University Founder Group Corp, Beijing 100871, China
Keywords:
swarm intelligence; cuckoo search algorithm; metaheuristic algorithm; nest spawning; Levy flights; adaptive step size; chaotic; population diversity
CLC:
TP301.6
DOI:
10.11992/tis.201811005
Abstract:
To improve the searching ability and convergence rate and further accelerate the research and application process of the algorithm, a review on the basic principles and state of the art and a comparison with other swarm intelligent optimization algorithms are performed, and the development trend is presented here. First, the basic model and steps of the cuckoo search algorithm are elaborated. Then, the improved methods of the cuckoo search algorithms are discussed, such as algorithms based on the discovery probability and step-size control parameter, algorithms based on the adaptive step size, algorithms based on chaos theory, combination algorithms with other algorithms, algorithms based on population characteristics and variations, combined optimization strategy, and algorithms based on population diversity. Their main application fields and progress are also summarized. Next, the cuckoo search algorithm is compared with a genetic algorithm, ant colony optimization algorithm, particle swarm optimization algorithm, and artificial bee colony algorithm in terms of advantages, disadvantages, and applicable scope. Finally, the existing problems of the algorithm are pointed out, and the research direction is prospected.
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