一种阶段性策略自适应差分进化算法  

Differential Evolution Algorithm with Stage-based Strategy Adaption

作　　者：倪洪杰[1] 彭春祥 周晓根 俞立[1] NI Hong-jie;PENG Chun-xiang;ZHOU Xiao-gen;YU Li(College of Information Engineering,Zhejiang University of Technology,Hangzhou 310023,China)

机构地区：[1]浙江工业大学信息工程学院

出　　处：《计算机科学》2019年第B06期106-110,共5页Computer Science

基　　金：浙江省重点研发计划项目(2017C03060);国家自然科学基金(61573317)资助

摘　　要：针对差分进化算法的变异策略选择问题,提出一种阶段性策略自适应差分进化算法(SSADE)。首先,根据各个体与当前最优个体之间的平均距离衡量种群的拥挤度,进而估计种群的进化阶段;然后,将整个种群划分为多个子种群,并针对不同阶段的特性,设计子种群协同进化变异策略池;最后,根据各变异策略的历史成功信息,从对应的策略池中动态自适应地选择合适的变异策略,从而达到平衡全局探测和局部搜索的目的。在12个经典测试函数上的实验结果表明,所提SSADE算法在计算代价、可靠性、解的质量和扩展性方面优于现有主流算法。To solve the problem of the selection for the mutation strategy in differential evolution algorithm,this paper proposed a differential evolution with stage-based strategy adaption.Firstly,the crowding degree of the population is measured by the average distance between each individual and the best individual,and the evolution stage is estimated.Then,the population is divided into multiple sub-populations,and a pool mutation strategy based on the coevolution of sub-populations is designed for different stages.Finally,according to the historical success information of each strategy,a suitable strategy is adaptively selected from the corresponding strategy pool to balance the exploration and exploitation.Experimental results on 12 classical test functions show that the proposed algorithm is superior to the mainstream algorithm in terms of the computational cost,success rate,quality of the solution,and scalability.

关 键 词：差分进化 策略自适应 子种群 全局优化 协同进化 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]