loading page

Cat Swarm Optimization Algorithm - A Survey and Performance Evaluation
  • Aram M. Ahmed ,
  • Tarik A. Rashid ,
  • Soran AM. Saeed
Aram M. Ahmed
Author Profile
Tarik A. Rashid
University of Kurdistan Hewler

Corresponding Author:[email protected]

Author Profile
Soran AM. Saeed
Author Profile


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.
22 Jan 2020Published in Computational Intelligence and Neuroscience volume 2020 on pages 1-20. 10.1155/2020/4854895