| 摘要: |
| 为了解决布谷鸟搜索算法收敛速度较低、全局收敛效率不高的问题，提出了具有万有引力加速机理的布谷鸟算法.该算法基于万有引力搜索无需学习外部环境因素的变化亦能感知全局最优的特点，将布谷鸟巢穴等价为不同质量的个体，使其在优化过程中不仅遵循Levy飞行规律，而且遵循万有引力定律.不仅利用布谷鸟巢穴间存在的万有引力进行加速搜索，而且提出了一种概率变异的方法，增大了种群多样性，有效地平衡了算法的全局搜索能力和局部开采能力，提高了算法的全局搜索效率和收敛精度.通过算法的数学机理分析和26个基准测试函数实验结果表明，所提出的算法与其他改进智能优化算法比较，具有更优的性能. |
| 关键词: 布谷鸟搜索算法 加速度 万有引力 发现概率 |
| DOI：10.13328/j.cnki.jos.006056 |
| 分类号:TP18 |
| 基金项目:国家自然科学基金（61204122）；福建省中青年教师教育科研项目（JA15037）；福建省自然科学基金（2015J1263） |
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| Cuckoo Search Algorithm with Gravitational Acceleration Mechanism |
|
FU Wen-Yuan1,2,3
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1.College of Information Science and Engineering, Huaqiao Univesity, Xiamen 361021, China;2.Xiamen Key Laboratory of ASIC System(Huaqiao University), Xiamen 361008, China;3.Fujian Engineering Research Center of Motor Control and System Optimal Schedule, Xiamen 361002, China
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| Abstract: |
| In this paper, a new cuckoo search algorithm with gravitational acceleration search mechanism is presented to address low convergence rate and deteriorated search precision. The algorithm is fundamentally inspired by the fact that gravitational search can also get the global optimal without perceiving the change on the driving effect of external environment. Each of the cuckoo nests exerted on different quality not only follows the Levy flight law but also abides the law of universal gravitation during the process of optimization, which accelerates the convergence significantly due to the intrinsic gravitational attraction between individuals within the cuckoo nests. Furthermore, a new probability mutation approach is formally given to achieve a balance between the global and local search for the proposed algorithm. Consequently, the global convergence efficiency and search precision of the algorithm are significantly enhanced. Via mathematical analysis and 26 benchmark test functions, the proposed agorithm is competitive for the convergence rate and search precision in a comparison with other variants of intelligent optimization algorithm. |
| Key words: cuckoo search algorithm acceleration gravitation discovery probability |
