规范化(社会学)
优势(遗传学)
多目标优化
人口
帕累托原理
数学优化
进化算法
数学
计算机科学
生物
人口学
生物化学
社会学
人类学
基因
作者
Hisao Ishibuchi,Lie Meng Pang,Ke Shang
标识
DOI:10.1145/3583131.3590437
摘要
In the field of evolutionary multi-objective optimization, it is well known that dominance-based algorithms do not work well on many-objective problems. This is because almost all solutions in a population become non-dominated in early generations. Two approaches have been proposed to decrease the number of non-dominated solutions. One is to increase the dominated region by each solution: dominance modification. The other is to increase the correlation among objectives: objective modification. In this paper, first we show that these two approaches can be viewed as the same approach. We also explain that some regions of the Pareto front are dominated when the dominated region is increased. Next, we numerically examine the effects of dominance modification on the performance of NSGA-II on many-objective test problems. Through computational experiments, we demonstrate that its positive and negative effects are clearly shown by the hypervolume (HV) and inverted generational distance (IGD) indicators, respectively. Then, we discuss why these two indicators emphasize different effects of dominance modification using the optimal distribution of solutions for each indicator. Finally, we explain that objective space normalization is needed in dominance modification whereas it has no effects on the Pareto dominance relation.
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