基于新约束集成的差分进化算法  

Differential Evolutionary Algorithm Based on New Ensemble of Constraint Handing Techniques

作　　者：孙越泓[1,2] 王丹 Sun Yuehong;Wang Dan(School of Mathematical Sciences,Nanjing Normal University,Nanjing 210023,China;Jiangsu Key Laboratory for Numerical Simulation of Large Scale Complex Systems,Nanjing 210023,China)

机构地区：[1]南京师范大学数学科学学院,江苏南京210023 [2]江苏省大规模复杂系统数值模拟重点实验室,江苏南京210023

出　　处：《南京师大学报（自然科学版）》2019年第4期1-11,共11页Journal of Nanjing Normal University(Natural Science Edition)

基　　金：国家自然科学基金(11871279、61971234);教育部人文社会科学青年基金(12YJCZH179);江苏省教育厅高校自然科学研究重大项目(16KJA110001)

摘　　要：提出基于新约束集成的差分进化算法用于求解带约束的优化问题.在产生新个体的阶段,算法采用3种不同的突变策略.利用不同的约束处理技术对新个体进行选择,并通过引入局部搜索,增强算法局部寻优能力,避免算法陷入局部最优.该算法在CEC 2017的28个基准函数上进行数值实验,并且与其他较为先进的算法进行比较,实验结果显示,新算法在求解精度上表现较好.In this paper,a differential evolutionary algorithm based on new ensemble of constraint handing techniques is proposed to solve optimization problems with constraints. At the stage of generating new individuals,the algorithm adopts three different mutation strategies. Different constraint handing techniques are used to select new individuals,and local search is introduced to enhance the local optimization ability and avoid the algorithm falling into local optimum. Numerical experiments are carried out on 28 benchmark functions from CEC 2017 and compared with other advanced algorithms. The results show that the new algorithm performs better in solution accuracy.

关 键 词：约束优化 差分进化算法 约束处理技术集成 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] TN911.7[自动化与计算机技术—计算机科学与技术]