What is it about?
This paper presents an in-depth survey and performance evaluation of 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).
Photo by Justin Sinclair on Unsplash
Why is it important?
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).
Read the Original
This page is a summary of: Cat Swarm Optimization Algorithm: A Survey and Performance Evaluation, Computational Intelligence and Neuroscience, January 2020, Hindawi Publishing Corporation, DOI: 10.1155/2020/4854895.
You can read the full text:
The following have contributed to this page