布谷鸟搜索算法研究及其应用进展

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Title:

Overview of the cuckoo search algorithm and its applications

作者:

吴一全^1,2,3, 周建伟¹

1. 南京航空航天大学 电子信息工程学院，江苏 南京 211106;
2. 北京市测绘设计研究院 城市空间信息工程北京市重点实验室，北京 100038;
3. 北大方正集团有限公司 数字出版技术国家重点实验室，北京 100871

Author(s):

WU Yiquan^1,2,3, 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

关键词:

群体智能; 布谷鸟搜索算法; 启发式算法; 寄巢产卵; 莱维飞行; 自适应步长; 混沌; 种群多样性

Keywords:

swarm intelligence; cuckoo search algorithm; metaheuristic algorithm; nest spawning; Levy flights; adaptive step size; chaotic; population diversity

分类号:

TP301.6

DOI:

10.11992/tis.201811005

摘要:

为进一步加强布谷鸟算法的搜寻能力并提升收敛速度，加快对算法的研究与应用进程，综述了布谷鸟算法的原理、研究概况和其他同类群体智能优化算法的比较及发展趋势。首先给出了算法的基本模型和实现步骤；然后重点阐述了基于发现概率和步长控制量、基于自适应步长、基于混沌理论、与其他算法混合、基于种群特征和种群变异、结合优化策略及基于种群多样性等方面的改进方法，总结了算法的主要应用领域及其进展；随后将其与遗传算法、蚁群优化算法、粒子群优化算法及人工蜂群优化算法的优点、缺点及适用性诸方面进行了对比；最后指出了布谷鸟搜索算法尚存在的缺陷并对进一步的研究方向进行了展望。

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.