The Cheetah Optimizer stands as a formidable algorithm, meticulously designed to tackle complex optimization problems across multiple dimensions. Like their real-life counterparts, cheetahs employ three distinctive hunting strategies: searching, sitting-and-waiting, and attacking.
During the dynamic searching and relentless attacking phases, a myriad of models can be harnessed, ranging from random functions to other combinatorial methods. Notably, the Cheetah Optimizer has been rigorously pitted against a slew of optimization algorithms, including the Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Differential Evolution (DE), Teaching-Learning Optimization Algorithm (TLBO), Genetic Algorithm (GA), Whale Optimization Algorithm (WOA), and Jaya Algorithm.
In the crucible of comparison, the Cheetah Optimizer has consistently emerged triumphant, showcasing exceptional performance on problems with varying dimensions. Its proven efficacy establishes it as a compelling choice for addressing a diverse array of optimization challenges.
Source: Akbari MA, Zare M, Azizipanah-Abarghooee R, Mirjalili S, Deriche M. The cheetah optimizer: A nature-inspired metaheuristic algorithm for large-scale optimization problems. Scientific Reports. 2022 Jun 29;12(1):10953.
引用格式
Seyedali Mirjalili (2024). Cheetah Optimizer (https://www.mathworks.com/matlabcentral/fileexchange/130404-cheetah-optimizer), MATLAB Central File Exchange. 检索来源 .
MATLAB 版本兼容性
创建方式
R2023a
兼容任何版本
平台兼容性
Windows macOS Linux标签
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!CO
版本 | 已发布 | 发行说明 | |
---|---|---|---|
1.0.0 |