计算机科学 ›› 2019, Vol. 46 ›› Issue (6A): 89-92.

• 智能计算 • 上一篇    下一篇

基于动态自适应权重和柯西变异的蝙蝠优化算法

赵青杰1, 李捷1, 于俊洋1,2, 吉宏远1   

  1. 河南大学软件学院 河南 开封 4750041;
    北京邮电大学网络与交换技术国家重点实验室 北京1008762
  • 出版日期:2019-06-14 发布日期:2019-07-02
  • 通讯作者: 李 捷(1975-),男,博士,教授,CCF会员,主要研究方向为教育大数据、智能算法,E-mail:lijie_henu@163.com
  • 作者简介:赵青杰(1994-),男,硕士,主要研究方向为智能算法,E-mail:su__xiu@163.com;于俊洋(1982-),男,博士,讲师,主要研究方向为云计算、大数据;吉宏远(1993-),男,硕士,主要研究方向为智能算法。
  • 基金资助:
    本文受赛尔网络下一代互联网创新项目(NGII20160204),网络与交换技术国家重点实验室开放课题资助项目(SKLNST-2016-2-23)资助。

Bat Optimization Algorithm Based on Dynamically Adaptive Weight and Cauchy Mutation

ZHAO Qing-jie1, LI Jie1, YU Jun-yang1,2, JI Hong-yuan1   

  1. School of Software,Henan University,Kaifeng,Henan 475004,China1;
    State Key Laboratory of Network and Exchange Technology,Beijing University of Posts and Telecommunications,Beijing 100876,China2
  • Online:2019-06-14 Published:2019-07-02

摘要: 为了加快蝙蝠算法的收敛速度并提高寻优精度,提出一种基于动态自适应权重和柯西变异的蝙蝠优化算法。该算法在速度公式中加入了动态自适应权重,以动态地调整自适应权重的大小,加快算法的收敛速度。此外,该算法引入了柯西逆累积分布函数方法,在每次迭代时,能有效提高蝙蝠算法的全局搜索能力,避免陷入局部最优。对12个典型的测试函数进行仿真实验,结果表明,改进后的算法显著提高了寻优性能,具有较快的收敛速度和较高的寻优精度。

关键词: 蝙蝠算法, 动态自适应权重, 柯西变异, 收敛对比

Abstract: In order to speed up the convergence of bat algorithm and improve the accuracy of optimization,this paper proposed a bat optimization algorithm based on dynamic adaptive weight and Cauchy mutation.The algorithm adds dynamic adaptive weight to the speed formula and dynamically adjusts the size of the adaptive weight to speed up the convergence of the algorithm.In addition,the Cauchy inverse cumulative distribution function method can effectively improve the global search ability of bat algorithm and avoid falling into local optimum.The simulation results of 12 typical test functions show that the improved algorithm has better performance,faster convergence speed and higher optimization accuracy.

Key words: Bat algorithm, Cauchy mutation, Convergence contrast, Dynamically adaptive weight

中图分类号: 

  • TP301.6
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