পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| জেলিফিশ অনুসন্ধান অপ্টিমাইজার× | Slime Mould Algorithm× | |
|---|---|---|
| ক্ষেত্র | অনুকূলকরণ | অনুকূলকরণ |
| পরিবার | Machine learning | Machine learning |
| উদ্ভবের বছর≠ | 2022 | 2020 |
| প্রবর্তক≠ | Xueying Shi | Shimin Li |
| ধরন | Nature-inspired metaheuristic algorithm | Nature-inspired metaheuristic algorithm |
| মৌলিক উৎস≠ | 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 ↗ |
| অপর নাম | JSO | SMA |
| সম্পর্কিত≠ | 3 | 5 |
| সারসংক্ষেপ≠ | 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. |
| ScholarGateডেটাসেট ↗ |
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