自适应变异蝴蝶优化算法  

An adaptive mutation butterfly optimization algorithm

作　　者：黄学雨[1,2] 罗华 HUANG Xue-yu;LUO Hua(School of Software Engineering,Jiangxi University of Science and Technology,Nanchang 330013;Nanchang Key Laboratory of Virtual Digital Factory and Cultural Communications,Nanchang 330013;School of Information Engineering,Jiangxi University of Science and Technology,Ganzhou 341000,China)

机构地区：[1]江西理工大学软件工程学院,江西南昌330013 [2]南昌市虚拟数字工程与文化传播重点实验室,江西南昌330013 [3]江西理工大学信息工程学院,江西赣州341000

出　　处：《计算机工程与科学》2023年第6期1123-1133,共11页Computer Engineering & Science

基　　金：国家重点研发计划(2020YFB1713700)。

摘　　要：针对基本蝴蝶优化算法存在的收敛速度慢、求解精度低和易陷入局部最优等问题,提出一种自适应变异蝴蝶优化算法。首先,利用改进帐篷映射结合重心反向学习初始化种群,获得更好的初始解;其次,在位置更新处引入非线性惯性权重,平衡算法的全局搜索与局部搜索能力;最后,在算法运行过程中,根据群体适应度方差以及当前最优解大小来决定是否对当前最优解和最差解进行高斯变异二次寻优,增强算法跳出局部最优的能力。对12个基准测试函数的多种维度仿真实验结果表明,该算法在收敛速度、求解精度和寻优稳定性方面明显优于其他对比算法。In view of the problems of the basic butterfly optimization algorithm,such as slow convergence speed,low solution accuracy and being prone to local optimum,an adaptive mutation butterfly optimization algorithm is proposed.Firstly,improved tent map barycenter reverse learning is used to the population to gain a better initial solution.Secondly,the nonlinear inertial weight is introduced in the location update to balance the global search and local search capabilities of the algorithm.Finally,the variance of population fitness and the size of the current optimal solution determine whether to carry out Gaussian mutation quadratic optimization for the current optimal solution and the worst solution,in order to enhance the ability of the algorithm to jump out of the local optimum.The multi-dimensional simulation results of 12 benchmark functions show that the proposed algorithm is be obviously better than other alignment algorithms in convergence speed,solution accuracy and optimization stability.

关 键 词：蝴蝶优化算法 帐篷映射 重心反向学习 非线性惯性权重 高斯变异 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]