Comparer des méthodes

	Algorithme des chauves-souris	Optimisation par essaim particulaire (PSO)
Domaine	Optimisation	Optimisation
Famille	Process / pipeline	Process / pipeline
Année d'origine	2010	1995
Auteur d'origine	Xin-She Yang	—
Type	Population-based swarm intelligence	Population-based metaheuristic / swarm intelligence
Source fondatrice	Yang, X.-S. (2010). A new metaheuristic bat-inspired algorithm. Nature Inspired Cooperative Strategies for Optimization (NICSO), 65–74.	Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948.
Alias	BA, Bat-Inspired Algorithm, Echolocation-Based Optimization, Yarasa Algoritması	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)
Apparentées	3	6
Résumé	The Bat Algorithm (BA) is a nature-inspired metaheuristic optimization method proposed by Xin-She Yang in 2010. It mimics the echolocation behavior of microbats to balance global exploration and local exploitation. Each artificial bat adjusts its position, velocity, and emission frequency, with loudness and pulse rate dynamically controlling the transition from broad search to refined local tuning. BA is suited to continuous and combinatorial optimization problems across engineering, scheduling, and machine learning domains.	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.