Optimization plays an important role in tackling public health problems. Animal instincts can be used effectively to solve complex public health management issues by providing optimal or approximately optimal solutions to complicated optimization problems common in public health. BAT algorithm is an exemplary member of a class of nature-inspired metaheuristic optimization algorithms and designed to outperform existing metaheuristic algorithms in terms of efficiency and accuracy. It's inspiration comes from the foraging behavior of group of microbats that use echolocation to find their target in the surrounding environment. In recent years, BAT algorithm has been extensively used by researchers in the area of optimization, and various variants of BAT algorithm have been developed to improve its performance and extend its application to diverse disciplines. This paper first reviews the basic BAT algorithm and its variants, including their applications in various fields. As a specific application, we apply the BAT algorithm to a biostatistical estimation problem and show it has some clear advantages over existing algorithms.

翻译：优化在应对公共卫生问题中扮演着重要角色。通过为公共卫生领域中常见的复杂优化问题提供最优或近似最优解，动物本能可被有效用于解决复杂的公共卫生管理问题。BAT算法是受自然启发的元启发式优化算法家族中的一个典范成员，其设计目标是在效率和精度方面超越现有的元启发式算法。其灵感来源于微型蝙蝠群体利用回声定位在周围环境中寻找目标的觅食行为。近年来，BAT算法在优化领域得到了研究者的广泛应用，并且已发展出多种变体以提升其性能，并将其应用拓展至多学科领域。本文首先回顾了基本BAT算法及其变体，包括它们在各个领域的应用。作为具体应用实例，我们将BAT算法应用于一个生物统计估计问题，并证明其相较于现有算法具有明显优势。