Analysis of Parameter Settings for the Bat Algorithm Using Variance Evolution

Abstract

Parameter settings in evolutionary algorithms and metaheuristics are important because such parameter values can influence the performance of algorithms under evaluation. For a given algorithm, there are many different numerical experiments to show that the algorithm can work well in practice; however, in most cases there is no theoretical analysis of parameter settings. In this work, we show that theoretical analysis using the theory of dynamical systems and evolution of population variance can give some good results in terms of parameter ranges for the bat algorithm. We also show that results from numerical experiments are consistent with theoretical bounds. Such analyses can provide good insights from different perspectives about the algorithmic characteristics such as variance evolution, transition between exploration and exploitation as well as convergence behaviour.

14 pages, 5 figures, ICCS2026

References in corpus (5)

• Bat Algorithm: A Novel Approach for Global Engineering Optimization
• Nature-Inspired Optimization Algorithms: Challenges and Open Problems
• An Improved Discrete Bat Algorithm for Symmetric and Asymmetric Traveling Salesman Problems
• The Global Convergence Analysis of the Bat Algorithm Using a Markovian Framework and Dynamical System Theory
• Parameter Tuning of the Firefly Algorithm by Three Tuning Methods: Standard Monte Carlo, Quasi-Monte Carlo and Latin Hypercube Sampling Methods