Comparer des méthodes

	Optimiseur Runge-Kutta	Optimisation par essaim particulaire (PSO)
Domaine	Optimisation	Optimisation
Famille	Machine learning	Process / pipeline
Année d'origine	2023	1995
Auteur d'origine	Ayushi Khatri	—
Type	Mathematical metaheuristic algorithm	Population-based metaheuristic / swarm intelligence
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.	Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948.
Alias	RKO	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)
Apparentées	5	6
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.	Particle Swarm Optimization (PSO) is a population-based metaheuristic algorithm introduced by Kennedy and Eberhart in 1995, inspired by the collective movement of bird flocks and fish schools. Each candidate solution — called a particle — moves through the search space by updating its velocity and position based on its own best experience and the best experience of the entire swarm, enabling fast convergence across continuous optimization problems.