This study proposes a bald eagle search (BES) algorithm, which is a novel, nature-inspired meta-heuristic optimisation algorithm that mimics the hunting strategy or intelligent social behaviour of bald eagles as they search for fish. Hunting by BES is divided into three stages. In the first stage (selecting space), an eagle selects the space with the most number of prey. In the second stage (searching in space), the eagle moves inside the selected space to search for prey. In the third stage (swooping), the eagle swings from the best position identified in the second stage and determines the best point to hunt. Swooping starts from the best point and all other movements are directed towards this point. BES is tested by adopting a three-part evaluation methodology that (1) describes the benchmarking of the optimisation problem to evaluate the algorithm performance, (2) compares the algorithm performance with that of other intelligent computation techniques and parameter settings and (3) evaluates the algorithm based on mean, standard deviation, best point and Wilcoxon signed-rank test statistic of the function values. Optimisation results and discussion confirm that the BES algorithm competes well with advanced meta-heuristic algorithms and conventional methods.

中文翻译：

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新颖的元启发式秃鹰搜索优化算法

本研究提出了一种秃鹰搜索 (BES) 算法，这是一种新颖的、受自然启发的元启发式优化算法，可模仿秃鹰在搜索鱼类时的狩猎策略或智能社交行为。BES 的狩猎分为三个阶段。在第一阶段（选择空间），老鹰选择猎物数量最多的空间。在第二阶段（空间搜索），鹰在选定空间内移动以搜索猎物。在第三阶段（俯冲），鹰从第二阶段确定的最佳位置摆动，并确定最佳狩猎点。俯冲从最佳点开始，所有其他动作都指向该点。BES 的测试采用三部分评估方法：(1) 描述优化问题的基准来评估算法性能，(2) 将算法性能与其他智能计算技术和参数设置的性能进行比较，以及 (3) 评估该算法基于函数值的均值、标准差、最佳点和 Wilcoxon 符号秩检验统计量。优化结果和讨论证实，BES 算法与先进的元启发式算法和传统方法竞争良好。