我想很多研究多目标优化的人看到顶刊上的论文结果都会存在疑惑。为了让大家快速上手，我给做一个简单介绍。
注：图片来自于Learning Adaptive Differential Evolution Algorithm from Optimization Experiences by Policy Gradient一文。
在文章中也并没给出这些符号对应的计算方法，我还查阅了其他的论文，同样缺少这样的描述。应该有这样困惑的同学不在少数，下面，我给大家进行讲解。
正文部分
首先需要学习结果先验分析的内容，包括Signed rank test,Rank sum test,Friedman test。在一定的概率水平下，如果均值相等，取“=”；如果结果显著且优于提出的算法，取“+”，不然就会取“-”。
In statistics, the Mann–Whitney U test (also called the Mann–Whitney–Wilcoxon (MWW), Wilcoxon rank-sum test, or Wilcoxon–Mann–Whitney test) is a nonparametric test.
This test can be used to determine whether two independent samples were selected from populations having the same distribution.
A Wilcoxon signed-rank test is a nonparametric test that can be used to determine whether two dependent samples were selected from populations having the same distribution.
一般表格下方会对每一列结果统计。
也有论文中给出如下描述，供大家写论文时参考。
WHERE THE BEST RESULT IN EACH ROW IS HIGHLIGHTED AND “+,” “−,” AND “=” INDICATE THAT THE RESULT IS SIGNIFICANTLY BETTER, SIGNIFICANTLY WORSE, AND STATISTICALLY SIMILAR TO THE ORIGINAL ALGORITHM
Reference
Wilcoxon, Frank. “Individual Comparisons by Ranking Methods.” Biometrics Bulletin 1.6(1945):80-83
Wikipedia. Wilcoxon rank-sum test
Wikipedia. Wilcoxon signed-rank test
百度百科. Wilcoxon 符号秩检验临界表
