পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| পার্টিকেল সোয়ার্ম অপ্টিমাইজেশন (PSO)× | Slime Mould Algorithm× | |
|---|---|---|
| ক্ষেত্র | অনুকূলকরণ | অনুকূলকরণ |
| পরিবার≠ | Process / pipeline | Machine learning |
| উদ্ভবের বছর≠ | 1995 | 2020 |
| প্রবর্তক≠ | — | Shimin Li |
| ধরন≠ | Population-based metaheuristic / swarm intelligence | Nature-inspired metaheuristic algorithm |
| মৌলিক উৎস≠ | Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948. DOI ↗ | 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≠ | PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO) | SMA |
| সম্পর্কিত≠ | 6 | 5 |
| সারসংক্ষেপ≠ | Particle Swarm Optimization (PSO) is a population-based metaheuristic algorithm introduced by Kennedy and Eberhart in 1995, inspired by the collective movement of bird flocks and fish schools. Each candidate solution — called a particle — moves through the search space by updating its velocity and position based on its own best experience and the best experience of the entire swarm, enabling fast convergence across continuous optimization problems. | 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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