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。
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.
作者
赵青杰
李捷
于俊洋
吉宏远
ZHAO Qing-jie;LI Jie;YU Jun-yang;JI Hong-yuan(School of Software,Henan University,Kaifeng,Henan 475004,China;State Key Laboratory of Network and Exchange Technology,Beijing University of Posts and Telecommunications,Beijing 100876,China)
出处
《计算机科学》
CSCD
北大核心
2019年第B06期89-92,共4页
Computer Science
基金
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
关键词
蝙蝠算法
柯西变异
动态自适应权重
收敛对比
Bat algorithm
Cauchy mutation
Dynamically adaptive weight
Convergence contrast
