# Bat Algorithm: Literature Review and Applications

arXiv: https://arxiv.org/abs/1308.3900 · HTML: https://mlsanity.com/paper/1308.3900/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Aug 2013.

## Built on by (most cited first, 5 of 5)

- [Artificial Collective Intelligence Engineering: a Survey of Concepts and Perspectives](https://mlsanity.com/paper/2304.05147/) (2023)
- [Swarm Intelligence for Self-Organized Clustering](https://mlsanity.com/paper/2106.05521/) (2021)
- [Feature Reduction Method Comparison Towards Explainability and Efficiency in Cybersecurity Intrusion Detection Systems](https://mlsanity.com/paper/2303.12891/) (2023)
- [A Three-Phase Artificial Orcas Algorithm for Continuous and Discrete Problems](https://mlsanity.com/paper/2302.08855/) (2023)
- [Two new feature selection methods based on learn-heuristic techniques for breast cancer prediction: A comprehensive analysis](https://mlsanity.com/paper/2407.14631/) (2024)

## Abstract (arXiv)

Bat algorithm (BA) is a bio-inspired algorithm developed by Yang in 2010 and BA has been found to be very efficient. As a result, the literature has expanded significantly in the last 3 years. This paper provides a timely review of the bat algorithm and its new variants. A wide range of diverse applications and case studies are also reviewed and summarized briefly here. Further research topics are also discussed.