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.