Cat Swarm Optimization Algorithm: A Survey and Performance Evaluation

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Metaheuristics

Background:

• The Cat Swarm Optimization (CSO) algorithm is a popular metaheuristic approach.
• Existing literature lacks a comprehensive survey and performance evaluation of CSO and its variants.
• This study addresses the need for a consolidated review and empirical assessment of CSO.

Purpose of the Study:

• To conduct an in-depth survey of Cat Swarm Optimization (CSO) algorithm developments and applications.
• To perform a rigorous performance evaluation of CSO against contemporary optimization algorithms.
• To provide a benchmark for future research on CSO.

Main Methods:

• A systematic literature review was performed to identify and categorize CSO variants and applications.
• CSO was tested on 23 classical and 10 modern (CEC 2019) benchmark functions.
• Performance comparison was conducted against Dragonfly Algorithm (DA), Butterfly Optimization Algorithm (BOA), and Fitness Dependent Optimizer (FDO) using Friedman test.

Main Results:

• The survey identified numerous developments and applications of the CSO algorithm.
• CSO demonstrated superior performance across the tested benchmark functions.
• Statistical analysis, including the Friedman test, confirmed CSO's leading performance compared to DA, BOA, and FDO.

Conclusions:

• Cat Swarm Optimization (CSO) is a highly effective and robust metaheuristic algorithm.
• CSO significantly outperforms other leading optimization algorithms on a wide range of benchmark problems.
• This work provides strong evidence for the practical applicability and superiority of CSO.