全局扰动和互利因子作用的飞蛾扑火优化算法  

Moth-flame optimization algorithm with global disturbance factor and mutually beneficial factor

作　　者：靳储蔚 李姗鸿 张琳娜[2] 张达敏 JIN Chu-wei;LI Shan-hong;ZHANG Lin-na;ZHANG Da-min(College of Big Data and Information Engineering,Guizhou University,Guiyang 550025,China;College of Mechanical Engineering,Guizhou University,Guiyang 550025,China)

机构地区：[1]贵州大学大数据与信息工程学院,贵州贵阳550025 [2]贵州大学机械工程学院,贵州贵阳550025

出　　处：《计算机工程与设计》2023年第8期2297-2304,共8页Computer Engineering and Design

基　　金：国家自然科学基金项目(62062021、61872034);贵州省科学技术基金项目(黔科合基础[2020]1Y254)。

摘　　要：为解决飞蛾扑火优化(moth-flame optimization, MFO)算法收敛速度慢、容易陷入局部最优等问题,提出一种飞蛾扑火优化(DBMFO)算法。使用Bernoulli混沌映射,提高初始种群的多样性;引入全局扰动因子,提高算法的全局搜索能力;使用互利因子对全局扰动后的位置再次进行更新,避免新的算法陷入局部最优,使得算法更快收敛。通过对10个基准函数进行仿真实验,确定迭代系数的取值,通过Wilcoxon秩和检验来验证算法性能,其结果表明,改进的DBMFO算法在求解的精确度以及收敛速度上均有明显提升。To solve the problems of low convergence speed and easiness to fall into local optimum of moth-flame optimization(MFO)algorithm,a moth-flame optimization(DBMFO)algorithm was proposed.Bernoulli chaotic map was used to improve the diversity of the initial population.A global disturbance factor was introduced to improve the global search ability of the algorithm.The mutually beneficial factor was used to update the position after the global disturbance again,so as to avoid the new algorithm from falling into the local optimum,so that the algorithm converged faster.Through the simulation experiments of 10 benchmark functions,the value of the iteration coefficient is determined,and the performance of the algorithm is verified by the Wilcoxon rank-sum test.The results show that the improved DBMFO algorithm can significantly improve the solution accuracy and convergence speed.

关 键 词：群智能算法 飞蛾扑火优化 伯努利混沌映射 全局扰动因子 互利因子 10个基准测试函数 秩和检验 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]