Σύγκριση μεθόδων

	Αλγόριθμος Μύκητα της Λάσπης	Aquila Optimizer
Πεδίο	Βελτιστοποίηση	Βελτιστοποίηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2020	2021
Δημιουργός	Shimin Li	Laith Abualigah
Τύπος	Nature-inspired metaheuristic algorithm	Nature-inspired metaheuristic algorithm
Θεμελιώδης πηγή	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.	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.
Εναλλακτικές ονομασίες	SMA	AO
Συναφείς	5	3
Σύνοψη	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.	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.