Comparer des méthodes

	Optimiseur Aquila	Optimisation par essaim particulaire (PSO)
Domaine	Optimisation	Optimisation
Famille	Machine learning	Process / pipeline
Année d'origine	2021	1995
Auteur d'origine	Laith Abualigah	—
Type	Nature-inspired metaheuristic algorithm	Population-based metaheuristic / swarm intelligence
Source fondatrice	Abualigah, L., Yousri, D., Abd Elaziz, M., Ewees, A. A., Al-qaness, M. A., & Gandomi, A. H. (2021). Aquila optimizer: A novel meta-heuristic optimization algorithm. Computers and Industrial Engineering, 157, 107250.	Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948.
Alias	AO	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)
Apparentées	3	6
Résumé	The Aquila Optimizer (AO) is a nature-inspired metaheuristic algorithm presented by Abualigah et al. in 2021, modeled after the hunting behavior and sensory abilities of golden eagles (aquila chrysaetos). The algorithm captures the exploration and exploitation phases of eagle hunting, including high-altitude soaring, exploration with high-precision vision, and rapid diving attacks. AO is designed to solve both constrained and unconstrained optimization problems.	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.