পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| মধু ব্যাজার অ্যালগরিদম× | পার্টিকেল সোয়ার্ম অপ্টিমাইজেশন (PSO)× | |
|---|---|---|
| ক্ষেত্র | অনুকূলকরণ | অনুকূলকরণ |
| পরিবার≠ | Machine learning | Process / pipeline |
| উদ্ভবের বছর≠ | 2023 | 1995 |
| প্রবর্তক≠ | Fatma A. Hashim | — |
| ধরন≠ | Nature-inspired metaheuristic algorithm | Population-based metaheuristic / swarm intelligence |
| মৌলিক উৎস≠ | Hashim, F. A., Hussain, K., & Houssein, E. H. (2023). Honey badger algorithm: A new meta-heuristic optimization algorithm. Neural Computing and Applications, 35(17), 12265-12287. link ↗ | Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948. DOI ↗ |
| অপর নাম≠ | HBA | PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO) |
| সম্পর্কিত≠ | 5 | 6 |
| সারসংক্ষেপ≠ | The Honey Badger Algorithm (HBA) is a nature-inspired metaheuristic optimization algorithm presented by Hashim et al. in 2023, modeled on the hunting behavior and intelligent strategies of honey badgers (Mellivora capensis). Honey badgers are known for their remarkable problem-solving abilities, fearlessness, and persistent pursuit of prey and food sources despite significant obstacles. HBA captures these behavioral traits to create an effective optimization framework. | 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. |
| ScholarGateডেটাসেট ↗ |
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