一种改进的多目标约束优化差分进化算法  

Improved multi-objective constrained optimization differential evolution algorithm

作　　者：龙文[1,2] 

机构地区：[1]贵州财经学院贵州省经济系统仿真重点实验室,贵阳550004 [2]贵州财经学院数学与统计学院,贵阳550004

出　　处：《计算机工程与应用》2012年第21期5-8,57,共5页Computer Engineering and Applications

基　　金：国家自然科学基金(No.61074069);贵州财经学院引进人才科研项目

摘　　要：提出一种新的多目标优化差分进化算法用于求解约束优化问题。该算法利用佳点集方法初始化个体以维持种群的多样性。将约束优化问题转化为两个目标的多目标优化问题。基于Pareto支配关系,将种群分为Pareto子集和Non-Pareto子集,结合差分进化算法两种不同变异策略的特点,对Non-Pareto子集和Pareto子集分别采用DE/best/1变异策略和DE/rand/1变异策略。数值实验结果表明该算法具有较好的寻优效果。A novel multi-objective optimization differential evolution algorithm is proposed for solving constrained optimization problems.In the process of population evolution,the individuals generation based on good-point-set method is introduced into the evolutionary algorithm initial step.The constrained optimization problem is converted into a multi-objective optimization problem.The population is divided into Non-Pareto set and Pareto set based on multi-objective optimization technique.In order to improve global convergence of the proposed algorithm,DE/best/1 mutation scheme and DE/rand/1 mutation scheme are used to the Non-Pareto set and the Pareto set respectively.The experimental results show that the proposed algorithm can get high performance while dealing with various complex problems.

关 键 词：约束优化问题 差分进化算法 多目标优化 佳点集 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]