解决复杂Pareto解集问题的进化算法  

Evolutionary Algorithm on Solving Complex Pareto Set Problems

作　　者：曾映兰 郑金华[1] 伍军[1] 罗彪[1] 

机构地区：[1]湘潭大学信息工程学院,湖南湘潭411105

出　　处：《计算机工程》2011年第7期199-200,203,共3页Computer Engineering

基　　金：国家自然科学基金资助项目(60773047);湖南省教育厅科研基金资助项目(09C961)

摘　　要：针对各种进化算法在解决PS问题上表现出来的脆弱性,提出一种解决复杂PS问题的自适应多目标差分进化算法SA-MODE。根据随机选择的父个体X与当前种群中的个体Y的支配关系,通过改变缩放因子的大小来控制新个体和父个体的距离。当X支配Y则新个体接近X,反之远离X,当X与Y互相不支配则产生2个新个体,一个接近X一个远离X。实验结果表明,在处理复杂PS问题时,SA-MODE与GDE3和NSGA-II相比有更理想的效果。After a deep analysis of the faults of traditional MOEAs on solving the complex Pareto Set（PS） problems,this paper proposes a multi-objective evolutionary algorithm on solving the complex PS problems（SA-MODE）.According to the dominated relationship between individual X which is chosen in the parent population and the individual Y in the current population,control the distance between new individual and the parent one by altering the size of scaling factor.The new individual is close to X when X dominates Y,the other hand away from X.When the X and Y do not dominate each other then create two new individuals,one near the X and the other from X.Experimental results demonstrate that SA-MODE can deal with complex PS problems more effectively compared with GDE3 and NSGA-II.

关 键 词：多目标优化问题 多目标差分进化算法 复杂Pareto解集问题 变量变换 变异算子 

分 类 号：TP393.02[自动化与计算机技术—计算机应用技术]