Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Optimizator prin Căutare de Meduze× | Algoritmul Mucegaiului de Nămol× | |
|---|---|---|
| Domeniu | Optimizare | Optimizare |
| Familie | Machine learning | Machine learning |
| Anul apariției≠ | 2022 | 2020 |
| Autorul original≠ | Xueying Shi | Shimin Li |
| Tip | Nature-inspired metaheuristic algorithm | Nature-inspired metaheuristic algorithm |
| Sursa seminală≠ | 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. link ↗ | 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. DOI ↗ |
| Denumiri alternative | JSO | SMA |
| Înrudite≠ | 3 | 5 |
| Rezumat≠ | 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. |
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