计算机科学 ›› 2020, Vol. 47 ›› Issue (11A): 68-72.doi: 10.11896/jsjkx.200200063

• 人工智能 • 上一篇    下一篇

基于余弦控制因子和迭代局部搜索的蝙蝠优化算法

郑浩1, 于俊洋1,2, 魏上斐1   

  1. 1 河南大学软件学院 河南 开封 475004
    2 河南省智能数据工程研究中心 河南 开封 475004
  • 出版日期:2020-11-15 发布日期:2020-11-17
  • 通讯作者: 于俊洋(jyyu@henu.edu.cn)
  • 作者简介:15516558060@163.com
  • 基金资助:
    国家自然科学基金(61672209)

Bat Optimization Algorithm Based on Cosine Control Factor and Iterative Local Search

ZHENG Hao1, YU Jun-yang1,2, WEI Shang-fei1   

  1. 1 School of Software,Henan University,Kaifeng,Henan 475004,China
    2 Henan Intelligent Data Engineering Research Center,Kaifeng,Henan 475004,China
  • Online:2020-11-15 Published:2020-11-17
  • About author:ZHENG Hao,born in 1996,postgraduate.His main research interests include Intelligent algorithm.
    YU Jun-yang,born in 1982,Ph.D,professor,is a member of China Computer Federation.His main research interests include cloud computing and big data.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61672209).

摘要: 针对蝙蝠算法寻优精度低、易陷入局部极值、求解不稳定的问题,提出了一种基于余弦控制因子和迭代局部搜索策略的蝙蝠的算法。首先在蝙蝠速度公式中加入由余弦因子控制的非线性惯性权重,来动态调节算法全局搜索与局部搜索的平衡,提高算法寻优精度和稳定性。其次,在每轮迭代结束时引入迭代局部搜索策略,扰动局部最优解获得中间状态,并重新搜索上述中间状态得到全局最优解,使算法快速跳出局部最优解,找到全局理论最优。最后与其他算法在12个复杂基准函数上进行仿真实验。结果表明,改进后的算法较好地解决了蝙蝠算法寻优精度不高、易陷入局部极值和求解不稳定的问题。

关键词: 蝙蝠算法, 迭代局部搜索策略, 扰动, 余弦控制因子

Abstract: To solve the problem that bat algorithm is easy to fall into local optimal solution when solving high-dimensional complex problems,an improved bat algorithm is proposed in this paper.Firstly,the nonlinear inertia weight controlled by cosine factor is added to the bat velocity formula to dynamically adjust the balance between global search and local search,so as to improve the accuracy and stability of the algorithm.Secondly,at the end of each iteration,the concept of iterated local search is introduced to perturb the local optimal solution to obtain the intermediate state,and then re-search the intermediate state to get the global optimal solution,which can make it jump out of the local optimal solution quickly.Finally,the simulation results on 12 complex benchmark functions with other algorithms show that the improved algorithm solves the problems of low precision,easy to fall into local extremum and unstable solution.

Key words: Bat algorithm, Cosine control factor, Disturbance, Iterative local search

中图分类号: 

  • TP301.6
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