Emperor Penguin Optimizer (EPO) is a metaheuristic algorithm which is recently developed and illustrates the emperor penguin’s huddling behaviour. However, the original version of the EPO will fix issues that are continuing in fact but not discrete. The eight separate EPO variants have been provided in this article. Four transfer features, s-shaped and v-shaped, that are used in order to map the search space into a separate research space are considered in the proposed algorithm. The output of the proposed algorithm is validated using 25 standard benchmark functions. It also analyses the statistical sense of the proposed algorithm. Experimental findings and comparisons suggest that the proposed algorithm performs better than other algorithms. The solution also applies to the issue of feature selection. The findings reveal the supremacy of the binary emperor penguin optimization algorithm.

Emperor Penguin Optimizer（EPO）是一种新启发式算法，最近得到开发，它说明了Emperor Penguin的拥挤行为。但是，EPO的原始版本将解决实际上仍在继续但并非离散的问题。本文提供了八个单独的EPO变体。在该算法中考虑了四个传递特征，即S形和v形，用于将搜索空间映射到单独的研究空间中。使用25个标准基准函数验证了所提出算法的输出。它还分析了该算法的统计意义。实验结果和比较结果表明，所提算法的性能优于其他算法。该解决方案也适用于功能选择问题。