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Cat Swarm Optimization Algorithm - A Survey and Performance Evaluation

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posted on 23.01.2020 by Aram M. Ahmed, Tarik A. Rashid, Soran AM. Saeed
This paper presents an in-depth survey and performance evaluation of the Cat Swarm Optimization (CSO) Algorithm. CSO is a robust and powerful metaheuristic swarm-based optimization approach that has received very positive feedback since its emergence. It has been tackling many optimization problems and many variants of it have been introduced. However, the literature lacks a detailed survey or a performance evaluation in this regard. Therefore, this paper is an attempt to review all these works, including its developments and applications, and group them accordingly. In addition, CSO is tested on 23 classical benchmark functions and 10 modern benchmark functions (CEC 2019). The results are then compared against three novel and powerful optimization algorithms, namely Dragonfly algorithm (DA), Butterfly optimization algorithm (BOA) and Fitness Dependent Optimizer (FDO). These algorithms are then ranked according to Friedman test and the results show that CSO ranks first on the whole. Finally, statistical approaches are employed to further confirm the outperformance of CSO algorithm.

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in Computational Intelligence and Neuroscience

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Email Address of Submitting Author

tarik.ahmed@ukh.edu.krd

ORCID of Submitting Author

https://orcid.org/0000-0002-8661-258X

Submitting Author's Institution

University of Kurdistan Hewler

Submitting Author's Country

Iraq

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Exports

Read the peer-reviewed publication

in Computational Intelligence and Neuroscience

Licence

Exports