Cat Swarm Optimization Algorithm: A Survey and Performance Evaluation.

Aram M Ahmed,Soran Ab M Saeed,Tarik A Rashid

doi:10.1155/2020/4854895

Abstract

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). 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.

Highlights

Optimization is the process by which the optimal solution is selected for a given problem among many alternative solutions
IEEE 6-bus and 14-bus networks were used in the simulation experiments and desirable results were achieved IEEE 57-bus system was used in the simulation experiments, in which Adaptive Dynamic Cat Swarm Optimization (ADCSO) outperformed 16 other optimization algorithms IEEE 14-bus and IEEE 24-bus systems were used in the simulation experiments, in which the system provided better results after adopting Cat swarm optimization (CSO)
E model performed better compared to a similar model, which was based on Binary particle swarm optimization (BPSO) and VSM

Summary

Introduction

Optimization is the process by which the optimal solution is selected for a given problem among many alternative solutions. In trajectory-based types, such as a simulated annealing algorithm [2], only one agent is searching in the search space to find the optimal solution, whereas, in the population-based algorithms, known as swarm Intelligence, such as particle swarm optimization (PSO) [3], multiple agents are searching and communicating with each other in a decentralized manner to find the optimal solution. Agents usually move in two phases, namely, exploration and exploitation. Having a trade-off between these two phases, in any algorithm, is very crucial because biasing towards either exploration or exploitation would degrade the overall performance and produce undesirable results [1]. Erefore, more than hundreds of swarm intelligence algorithms have been proposed by researchers to achieve this balance and provide better solutions for the existing optimization problems Having a trade-off between these two phases, in any algorithm, is very crucial because biasing towards either exploration or exploitation would degrade the overall performance and produce undesirable results [1]. erefore, more than hundreds of swarm intelligence algorithms have been proposed by researchers to achieve this balance and provide better solutions for the existing optimization problems

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Conclusion