摘要
针对飞蛾扑火算法求解大规模优化问题较差的实际,借鉴差分进化算法中的变异思想,在飞蛾扑火算法中引入缩放因子和视距因子的概念,提出飞蛾直飞模型,并界定围绕历史最优飞蛾和当前随机飞蛾的直飞方式分别为局部寻优和全局寻优;设计3种不同类型的视距因子,从宏观上引导搜索算法启动全局探索和局部开发的时机,分析不同启动时机选择对飞蛾扑火算法在大规模问题上的优化精度影响,提出不同优化问题具有不同启动时机的思想;讨论飞蛾直飞和螺旋式飞行的3种组合策略下的优化效率,验证了所提出算法的较优性能,与现有文献改进算法在大规模优化问题上的改进效果进行对比,数值实验验证了改进算法的优越性和鲁棒性,拓展和丰富了原算法的应用范围.
In view of the fact that the moth-flame optimization(MFO)algorithm is poor in solving large-scale optimization problems,the direct flight model of moths is proposed based on the mutation idea of differential evolution algorithm(DE).The concept of the sight distance factor and the scaling factor is used,and the flame around the historical optimal moth and the current random moth is defined as local development and global exploration.Three different types of the sight distance factor are designed to guide the search algorithm and the global exploration and local development from the macroscopic point of view are started.The influence of different start-up time on the precision of the MFO algorithm in the large-scale problem is analyzed,and the idea that different optimization problems have different start-up opportunity for local development.The optimization efficiency of the three combined strategies is discussed on the model of direct flight and helical flight,and the better performance of the proposed algorithm is verified.Numerical experiments vprify the superiority and robustness of the improved algorithm,compared with the existing literature on large-scale optimization problems.
作者
刘小龙
LIU Xiao-long(School of Business Administration,South China University of Technology,Guangzhou 510641,China)
出处
《控制与决策》
EI
CSCD
北大核心
2020年第4期901-908,共8页
Control and Decision
基金
国家自然科学基金项目(71071057,71571072)
广州社科联基金项目(2018GZGJ02,2016GZYB10).
关键词
大规模优化问题
飞蛾扑火优化算法
函数优化
视距因子
缩放因子
统计指导
large scale optimization problem
moth-flame optimization algorithm
function optimization
sight distance factor
scaling factor
statistical guidance
作者简介
通讯作者:刘小龙(1977-),男,讲师,博士,从事仿生优化、智能优化算法等研究,E-mail:xlliu@scut.edu.cn.
