计算机科学 ›› 2017, Vol. 44 ›› Issue (5): 235-240.doi: 10.11896/j.issn.1002-137X.2017.05.042

• 人工智能 • 上一篇    下一篇

自适应步长布谷鸟搜索算法

李荣雨,戴睿闻   

  1. 南京工业大学计算机科学与技术学院 南京211800,南京工业大学计算机科学与技术学院 南京211800
  • 出版日期:2018-11-13 发布日期:2018-11-13
  • 基金资助:
    本文受江苏省高校自然科学基金资助

Adaptive Step-size Cuckoo Search Algorithm

LI Rong-yu and DAI Rui-wen   

  • Online:2018-11-13 Published:2018-11-13

摘要: 布谷鸟搜索算法(CSA)是一种新颖且简单、高效的生物启发式算法。针对标准算法存在后期收敛速度慢、易陷入局部最优等问题,提出了一种新的自适应步长布谷鸟搜索算法(ASCSA)。通过自适应调整莱维飞行步长使算法在前期拥有较大的寻优空间,提高全局搜索能力;步长随迭代自适应减小,算法的局部开发能力增强。针对偏好随机游动,引入动态惯性权重和记忆策略后,算法能够充分利用历史经验,稳定性得到提高。实验结果表明,改进后的布谷鸟搜索算法的各方面性能较标准算法及相关改进版本都有显著提高。

关键词: 布谷鸟搜索算法,莱维飞行,自适应步长,动态惯性权重,记忆策略

Abstract: Cuckoo search algorithm (CSA) is a novel nature-inspired algorithm which is simple and efficient.To overcome the defections that standard algorithm has slow convergence rate and falls into local optimum easily in the later period,a new adaptive step-size cuckoo search algorithm(ASCSA) was proposed.By adjusting the step-size of lévy flight adaptively,the algorithm enhances the ability of global search in the earlier period and the local search in the later pe-riod.What‘s more,for the bias random walk,by introducing the dynamic inertial weight and memory strategy,the introduced algorithm can make full use of historical experience.The stability of algorithm has been strengthened.Simulation results show that the performance of ASCSA is obviously improved by compared with the standard CS algorithm and modified ones.

Key words: Cuckoo search algorithm,Lévy flight,Adaptive step-size,Dynamic inertia weight,Memory strategy

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