Nature-inspired optimization algorithms especially those based on the hunting behaviors of the creatures assume that the hunting operations are performed in a safe environment. However, generally, there are threats in real-life for the hunter-animals. This paper focuses on these threat factors and proposes that they can be used to improve the searching abilities of the algorithms. Gray wolf optimization (GWO) algorithm was selected to present the proposed approach and it was assumed that there was a mountain lion as the threat factor living in the same habitat with the wolf pack. The relations between the two predators were modeled and used to improve the performance of the algorithm. Five experiments were conducted to test the performance of the proposed method and the results were compared with the GWO and four optimization algorithms from the literature. It is shown that the proposed algorithm obtained best results for 21 of the 50 benchmark functions, while its closest competitor achieved the best results for 16 functions. Besides, the results of the Wilcoxon signed-rank test indicated that the proposed method is superior to all other methods. In addition, it was shown that the threat factor approach does not cause a significant increase in the processing time.

中文翻译：

---

一种基于自然优化算法的新方法：威胁因素方法

受自然启发的优化算法，尤其是那些基于生物狩猎行为的优化算法，假设狩猎操作是在安全的环境中进行的。然而，一般来说，狩猎动物在现实生活中存在威胁。本文着眼于这些威胁因素，并提出可以利用它们来提高算法的搜索能力。选择了灰狼优化 (GWO) 算法来展示所提出的方法，并假设有一只美洲狮作为威胁因素与狼群生活在同一栖息地。两个捕食者之间的关系被建模并用于提高算法的性能。进行了五个实验来测试所提出方法的性能，并将结果与​​ GWO 和文献中的四种优化算法进行比较。结果表明，所提出的算法在 50 个基准函数中的 21 个获得了最佳结果，而其最接近的竞争对手在 16 个函数上获得了最佳结果。此外，Wilcoxon 符号秩检验的结果表明所提出的方法优于所有其他方法。此外，结果表明威胁因素方法不会导致处理时间显着增加。Wilcoxon 符号秩检验的结果表明所提出的方法优于所有其他方法。此外，结果表明威胁因素方法不会导致处理时间显着增加。Wilcoxon 符号秩检验的结果表明所提出的方法优于所有其他方法。此外，结果表明威胁因素方法不会导致处理时间显着增加。