Comparer des méthodes

	Optimiseur Runge-Kutta	Optimisation par les buses de Harris
Domaine	Optimisation	Optimisation
Famille	Machine learning	Machine learning
Année d'origine	2023	2019
Auteur d'origine	Ayushi Khatri	Ali Asghar Heidari
Type	Mathematical metaheuristic algorithm	Nature-inspired metaheuristic algorithm
Source fondatrice	Khatri, A., Kumar, A., & Gaba, G. K. (2023). Runge Kutta optimizer: An efficient approach for solving optimization tasks. Computers and Industrial Engineering, 180, 109201.	Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 97, 849-872.
Alias	RKO	HHO
Apparentées	5	4
Résumé	The Runge Kutta Optimizer (RKO) is a metaheuristic algorithm introduced by Khatri et al. in 2023 that leverages numerical integration principles from the Runge-Kutta method. Instead of biological inspiration, RKO grounds optimization in mathematical principles of differential equations and numerical integration. The algorithm treats the optimization landscape as a dynamic system and uses multi-stage integration steps to evolve solutions toward optima.	Harris Hawks Optimization (HHO) is a metaheuristic algorithm introduced by Heidari et al. in 2019, inspired by the hunting strategies of Harris's hawks. The algorithm models the cooperative hunting behavior and escape strategies of these raptors to solve complex optimization problems. HHO balances exploration through perching and exploitation through dynamic pursuit, making it effective for multimodal and high-dimensional optimization.