解决复杂Pareto解集问题的进化算法

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。

Evolutionary Algorithm on Solving Complex Pareto Set Problems

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.