পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| পার্টিকেল সোয়ার্ম অপ্টিমাইজেশন (PSO)× | গ্রে উলফ অপ্টিমাইজার× | |
|---|---|---|
| ক্ষেত্র | অনুকূলকরণ | অনুকূলকরণ |
| পরিবার | Process / pipeline | Process / pipeline |
| উদ্ভবের বছর≠ | 1995 | 2014 |
| প্রবর্তক≠ | — | Seyedali Mirjalili, Seyed Mohammad Mirjalili, Andrew Lewis |
| ধরন≠ | Population-based metaheuristic / swarm intelligence | Swarm-intelligence metaheuristic |
| মৌলিক উৎস≠ | Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948. DOI ↗ | Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46-61. DOI ↗ |
| অপর নাম | PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO) | GWO, Gri Kurt Optimizasyonu, Gri Kurt Optimizasyonu (GWO) |
| 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 Grey Wolf Optimizer (GWO) is a swarm-intelligence metaheuristic introduced by Mirjalili, Mirjalili, and Lewis in 2014 that models the social hierarchy and cooperative hunting behaviour of grey wolves. A population of candidate solutions is divided into four leadership ranks — alpha, beta, delta, and omega — and the three best solutions at each iteration guide the entire swarm toward increasingly better regions of the search space. |
| ScholarGateডেটাসেট ↗ |
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