求解高维复杂函数的改进飞蛾扑火算法  

An Improved Moth-flame Optimization Algorithm for Solving High-dimensional Complex Functions

作　　者：李煜[1] 朱新亚 刘景森[3] LI Yu;ZHU Xinya;LIU Jingsen(Institute of Management Science and Engineering,Henan University,Kaifeng 475004,China;School of Business,Henan University,Kaifeng 475004,China;Institute of Intelligent Network Systems,Henan University,Kaifeng 475004,China)

机构地区：[1]河南大学管理科学与工程研究所,河南开封475004 [2]河南大学商学院,河南开封475004 [3]河南大学智能网络系统研究所,河南开封475004

出　　处：《工业工程》2023年第2期101-110,共10页Industrial Engineering Journal

基　　金：国家自然科学基金资助项目(71601071);河南省重点研发与推广资助专项(222102210065);教育部人文社科青年基金资助项目(15YJC630079)。

摘　　要：提出一种融合自适应动态扰动系数和分段可调节搜索策略的飞蛾扑火优化算法(a moth-flame optimization algorithm with adaptive dynamic disturbance coefficient and piecewise adjustable search strategy,ADMFO),求解高维复杂函数优化问题。通过自适应动态扰动系数策略来提高算法的全局搜索能力,避免算法陷入局部最优;通过分段可调节搜索策略来平衡全局探索和局部开发的比重,以此实现更好的寻优策略。对15个单峰和多峰复杂高维基准函数进行寻优实验,与粒子群算法、正弦余弦算法、蝴蝶算法、灰狼算法和其他4种改进算法进行对比。实验结果表明,ADMFO算法具有更好的寻优精度和稳定性。A moth-flame optimization algorithm with adaptive dynamic disturbance coefficient and piecewise adjustable search strategy(ADMFO)is proposed to solve a large-scale complex optimization problem.The adaptive dynamic perturbation coefficient strategy is adopted to improve the global searching ability of the algorithm and avoid the algorithm falling into the local optimum.The segmented search strategy can balance the proportion of global exploration and local development,so as to achieve a better search strategy.15 unimodal and multi-peak complex high-dimensional function optimization experiments are conducted,comparing particle swarm algorithm,sine cosine algorithm,butterfly optimization algorithm,the gray wolf algorithm,and the four improved algorithms proposed in other literature.The experimental data proves that the improved algorithm has better optimization accuracy and stability.

关 键 词：飞蛾扑火优化算法 自适应动态扰动系数 分段可调节搜索 高维复杂函数优化 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]