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Home
Cat Swarm Optimization Algorithm: A Survey And Performance Evaluation.

Home
Cat Swarm Optimization Algorithm: A Survey And Performance Evaluation.

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Cat Swarm Optimization Algorithm: A Survey and Performance Evaluation.

Aram M Ahmed^1,2, Tarik A Rashid³, Soran Ab M Saeed²

¹International Academic Office, Kurdistan Institution for Strategic Studies and Scientific Research, Sulaymaniyah 46001, Iraq.

Computational Intelligence and Neuroscience

|May 15, 2020

View abstract on PubMed

Summary

This summary is machine-generated.

This study surveys and evaluates the Cat Swarm Optimization (CSO) algorithm, a powerful metaheuristic. Performance tests show CSO outperforms other algorithms on benchmark functions, confirming its effectiveness.

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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.