Krahasoni metodat

	Optimizuesi i Kërkimit të Meduzave	Algoritmi i Mykut të Mukozës
Fusha	Optimizimi	Optimizimi
Familja	Machine learning	Machine learning
Viti i origjinës	2022	2020
Krijuesi	Xueying Shi	Shimin Li
Lloji	Nature-inspired metaheuristic algorithm	Nature-inspired metaheuristic algorithm
Burimi themelues	Shi, X., Sun, Y., Zhan, Z. H., Yuen, K. F., & Zhang, J. (2022). Jellyfish search optimizer: A new bio-inspired metaheuristic algorithm for solving optimization tasks. Neural Computing and Applications, 34(10), 7651-7673.	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.
Emërtime të tjera	JSO	SMA
Të lidhura	3	5
Përmbledhja	The Jellyfish Search Optimizer (JSO) is a biologically-inspired metaheuristic algorithm introduced by Shi et al. in 2022, based on the movement and foraging behavior of jellyfish in ocean environments. Jellyfish exhibit two distinct behaviors: passive drifting with ocean currents (exploration) and active swimming toward food sources (exploitation). JSO captures these behaviors to create an effective balance between global search and local refinement.	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.