विधियों की तुलना करें

	एक्विला ऑप्टिमाइज़र	पार्टिकल स्वार्म ऑप्टिमाइज़ेशन (PSO)	स्लाइम मोल्ड एल्गोरिथम
क्षेत्र	अनुकूलन	अनुकूलन	अनुकूलन
परिवार	Machine learning	Process / pipeline	Machine learning
उद्भव वर्ष	2021	1995	2020
प्रवर्तक	Laith Abualigah	—	Shimin Li
प्रकार	Nature-inspired metaheuristic algorithm	Population-based metaheuristic / swarm intelligence	Nature-inspired metaheuristic algorithm
मौलिक स्रोत	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.	Li, S., Chen, H., Wang, M., Heidari, A. A., & Chakraborty, S. (2020). Slime mould algorithm: A new method for stochastic optimization. Future Generation Computer Systems, 111, 300-323.
उपनाम	AO	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)	SMA
संबंधित	3	6	5
सारांश	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.	The Slime Mould Algorithm (SMA) is a nature-inspired metaheuristic optimization technique introduced by Li et al. in 2020. It mimics the behavior of slime moulds, which spread and contract to find optimal food sources. SMA addresses complex optimization problems by simulating the adaptive foraging and spatial distribution patterns of these organisms.