Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (2): 55-61.DOI: 10.3778/j.issn.1002-8331.1901-0309

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Shared Crow Algorithm Using Multi-Segment Perturbation

XIN Ziyun, ZHANG Damin, CHEN Zhongyun, ZHANG Huijuan, YAN Wei   

  1. College of Big Data & Information Engineering, Guizhou University, Guiyang 550025, China
  • Online:2020-01-15 Published:2020-01-14



  1. 贵州大学 大数据与信息工程学院,贵阳 550025

Abstract: Crow search algorithm, as a new meta-heuristic intelligent algorithm, simulates the behavior of crows following each other to steal. In order to improve the convergence accuracy and late search ability of the algorithm, a new hybrid crow search algorithm is proposed based on the traditional crow search algorithm. Its core idea is to add a sharing mechanism in the algorithm, improve the position update method of random tracking in the original algorithm, reduce the blindness of search and improve the convergence speed. In different iteration stages, perturbation operations of different sizes are performed on the global optimal position, which effectively improves the probability of jumping out of the local optimal position and ensures the balance between the global search ability and the local search ability of the algorithm. Finally, through eight benchmark functions, the search performance of the five algorithms is compared and analyzed in 10, 30 and 50 dimensions. The results show that the comprehensive performance of the modified algorithm is better than other algorithms.

Key words: crow search algorithm, shared mechanism, multi-segment perturbation

摘要: 乌鸦搜索算法作为新提出的元启发式智能算法,其寻优方式模拟了乌鸦间相互跟随窃食的行为。为了提高算法的收敛精度、后期搜索能力等,基于传统乌鸦搜索算法提出一种新的混合乌鸦搜索算法,其核心思想是在算法中加入共享机制,改进原始算法中随机追踪的位置更新方式,降低搜索盲目性,提高收敛速度;在不同的迭代阶段对全局最优位置进行大小不同的扰动操作,有效提高了跳出局部最优的概率,保证算法全局搜索能力与局部搜索能力的平衡。最后通过8个基准函数对5种算法搜索性能在10、30、50维的情况下进行对比分析,结果表明该改进算法的综合表现要优于其他算法。

关键词: 乌鸦搜索算法, 共享机制, 多段扰动