计算机工程与应用2017，Vol.53Issue(4)：156-162,7.DOI:10.3778/j.issn.1002-8331.1506-0277
加权变异策略动态差分进化算法
Dynamic differential evolution algorithm with weighted mutation strategy
摘要
Abstract
Because of the problems of Differential Evolution(DE)algorithm such as premature convergence, low accuracy and tedious parameter setting for hard high-dimensional optimization problems, a dynamic differential evolution algorithm with weighted mutation strategy, called WMDDE, is presented. Firstly, two new weighted mutation operators of random disturbance are designed by weighting combination of DE/rand/1 and DE/best/1, which is utilized to balance the global and local search dynamically, and avoid premature convergence. Secondly, a self-adaptive parameter setting strategy of adjust scaling factor and crossover factor is designed, avoiding tedious parameter setting. Finally, experimental results on 11 benchmark functions show that the new algorithm can effectively avoid premature convergence and has the global con-vergence ability strongly, and its optimization rate solution accuracy, stability are better than the other four kinds of differ-ential evolutions.关键词
差分进化算法/维变异/扰动/早熟收敛/参数调整Key words
differential evolution algorithm/dimensional mutation/disturbance/premature convergence/parameter setting分类
信息技术与安全科学引用本文复制引用
张锦华,宋来锁,张元华,李富昌..加权变异策略动态差分进化算法[J].计算机工程与应用,2017,53(4):156-162,7.基金项目
国家自然科学基金(No.71262031). （No.71262031）
