What is it about?

Bat algorithm is given in-depth in terms of backgrounds, characteristics, limitations, it has also displayed the algorithms that hybridized with BA (K-Medoids, back-propagation neural network, harmony search algorithm, differential evaluation strategies, enhanced particle swarm optimization and Cuckoo search algorithm) and their theoretical results, as well as to the modifications that have been performed of the algorithm (modified bat algorithm, enhanced bat algorithm, bat algorithm with mutation (BAM), uninhabited combat aerial vehicle-BAM and non-linear optimization). It also provides a summary review that focuses on improved and new bat algorithm (directed artificial bat algorithm, complex-valued bat algorithm, principal component analyzes-BA, multiple strategies coupling bat algorithm and directional bat algorithm).

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Why is it important?

The purpose of this study is to provide the reader with a full study of the bat algorithm, including its limitations, the fields that the algorithm has been applied, versatile optimization problems in different domains and all the studies that assess its performance against other meta-heuristic algorithms.


As far as the research community knowledge, there is no comprehensive survey study conducted on this algorithm covering all its aspects.

Professor Tarik A. Rashid
University of Kurdistan Hewler

Read the Original

This page is a summary of: Critical analysis: bat algorithm-based investigation and application on several domains, World Journal of Engineering, January 2021, Emerald, DOI: 10.1108/wje-10-2020-0495.
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