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Metaheuristics is All You Need

Eliuvish Cuicizion, Haowen Xu, Weng Kee Wong

TL;DR

This paper surveys the Bat Algorithm (BA), a nature-inspired metaheuristic for solving high-dimensional optimization problems with applications in public health. It details the BA’s core mechanisms, including frequency-based exploration, velocity and position updates, and adaptive loudness and emission rates, along with practical parameter settings and comparisons to alternative heuristics. The authors illustrate BA’s utility across biostatistical estimation tasks—ranging from GLMs and log-binomial MLE to multi-state survival and Hawkes-process models—demonstrating competitive performance and robust applicability. They also catalog numerous BA variants that address discretization, convergence, and multiobjective challenges, underscoring BA’s flexibility for real-world health problems. Overall, the work argues that metaheuristics, exemplified by BA, offer a versatile toolkit for public-health optimization, capable of enhancing parameter estimation, model fitting, and complex decision-making when traditional methods struggle or are computationally prohibitive.

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.

Metaheuristics is All You Need

TL;DR

This paper surveys the Bat Algorithm (BA), a nature-inspired metaheuristic for solving high-dimensional optimization problems with applications in public health. It details the BA’s core mechanisms, including frequency-based exploration, velocity and position updates, and adaptive loudness and emission rates, along with practical parameter settings and comparisons to alternative heuristics. The authors illustrate BA’s utility across biostatistical estimation tasks—ranging from GLMs and log-binomial MLE to multi-state survival and Hawkes-process models—demonstrating competitive performance and robust applicability. They also catalog numerous BA variants that address discretization, convergence, and multiobjective challenges, underscoring BA’s flexibility for real-world health problems. Overall, the work argues that metaheuristics, exemplified by BA, offer a versatile toolkit for public-health optimization, capable of enhancing parameter estimation, model fitting, and complex decision-making when traditional methods struggle or are computationally prohibitive.

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.

Paper Structure

This paper contains 16 sections, 29 equations, 3 figures, 11 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Optimization Algorithm
  3. Heuristics and Metaheuristics
  4. BAT algorithm
  5. The Basic BAT Algorithm
  6. Parameter Setting of the BAT Algorithm
  7. Other Algorithms
  8. Variants of BAT algorithm
  9. Applications of Bat Algorithms in Public Health
  10. Parameter Estimation Problems in Models for Analyzing Data from Public Health
  11. Generalized Linear Models
  12. Find MLE for Log-binomial Model
  13. An Epidemiology Example with Non-convergence Issue of Relative Risk Regression
  14. Estimation and Inference in Markov Renewal Regression Model
  15. Parameter Estimation in Hawkes Process Models
  16. ...and 1 more sections

Figures (3)

  • Figure 1: Contour plots of the negative log likelihood functions from the three data sets. Left: MLE at boundary; middle: MLE at infinity; right: MLE at interior.
  • Figure 2: A six-state Markov renewal model for BMT failure. State transition diagram illustrating the possible transitions between clinical states in the EBMT dataset. Temporary states include TX (transplantation), PLT Recovery, Adverse Event, and Recovered and Adverse Event, while absorbing states are Alive in Relapse and Relapse/Death.
  • Figure 3: Negative log-likelihood, estimated parameters and $L_2$-error of 100 simulated Hawkes process estimates. BAT = Bat algorithm, CS = Cuckoo search, GA = Genetic algorithm, HS = Harmony search, PSO = Particle swarm optimization.