Metaheuristics is All You Need

TL;DR

This paper reviews the BAT metaheuristic algorithm inspired by microbats, discusses its variants, and demonstrates its effectiveness in biostatistical estimation problems, highlighting its advantages over other algorithms.

Contribution

It provides a comprehensive review of the BAT algorithm and its variants, and applies it to biostatistical estimation, showcasing its practical benefits.

Findings

BAT algorithm outperforms some existing algorithms in biostatistical estimation

Variants of BAT improve efficiency and accuracy in optimization tasks

The paper demonstrates BAT's applicability in public health-related optimization problems

Abstract

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.