A key aspect of the design of evolutionary and swarm intelligence algorithms is studying their performance. Statistical comparisons are also a crucial part which allows for reliable conclusions to be drawn. In the present paper we gather and examine the approaches taken from different perspectives to summarise the assumptions made by these statistical tests, the conclusions reached and the steps followed to perform them correctly. In this paper, we conduct a survey on the current trends of the proposals of statistical analyses for the comparison of algorithms of computational intelligence and include a description of the statistical background of these tests. We illustrate the use of the most common tests in the context of the Competition on single-objective real parameter optimisation of the IEEE Congress on Evolutionary Computation (CEC) 2017 and describe the main advantages and drawbacks of the use of each kind of test and put forward some recommendations concerning their use.

进化和群体智能算法设计的一个关键方面是研究它们的性能。统计比较也是至关重要的部分，可以得出可靠的结论。在本文中，我们收集并检查了从不同角度采取的方法，以总结这些统计检验所作的假设，得出的结论以及为正确执行这些假设而应采取的步骤。在本文中，我们对统计分析提案的当前趋势进行了调查，以比较计算智能算法，并对这些测试的统计背景进行了描述。