# The Global Convergence Analysis of the Bat Algorithm Using a Markovian Framework and Dynamical System Theory

arXiv: https://arxiv.org/abs/1903.11971 · HTML: https://mlsanity.com/paper/1903.11971/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2019.

## Built on by (most cited first, 1 of 1)

- [Analysis of Parameter Settings for the Bat Algorithm Using Variance Evolution](https://mlsanity.com/paper/2606.28644/) (2026)

## Abstract (arXiv)

The bat algorithm (BA) has been shown to be effective to solve a wider range of optimization problems. However, there is not much theoretical analysis concerning its convergence and stability. In order to prove the convergence of the bat algorithm, we have built a Markov model for the algorithm and proved that the state sequence of the bat population forms a finite homogeneous Markov chain, satisfying the global convergence criteria. Then, we prove that the bat algorithm can have global convergence. In addition, in order to enhance the convergence performance of the algorithm, we have designed an updated model using the dynamical system theory in terms of a dynamic matrix, and the parameter ranges for the algorithm stability are then obtained. We then use some benchmark functions to demonstrate that BA can indeed achieve global optimality efficiently for these functions.