Σύγκριση μεθόδων

	Αλγόριθμος Μύκητα της Λάσπης	Βελτιστοποίηση Γερακιών Harris
Πεδίο	Βελτιστοποίηση	Βελτιστοποίηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2020	2019
Δημιουργός	Shimin Li	Ali Asghar Heidari
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.	Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 97, 849-872.
Εναλλακτικές ονομασίες	SMA	HHO
Συναφείς	5	4
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.	Harris Hawks Optimization (HHO) is a metaheuristic algorithm introduced by Heidari et al. in 2019, inspired by the hunting strategies of Harris's hawks. The algorithm models the cooperative hunting behavior and escape strategies of these raptors to solve complex optimization problems. HHO balances exploration through perching and exploitation through dynamic pursuit, making it effective for multimodal and high-dimensional optimization.