পদ্ধতির তুলনা করুন
নির্বাচিত পদ্ধতিগুলো পাশাপাশি পর্যালোচনা করুন; যে সারিগুলোয় পার্থক্য আছে সেগুলো চিহ্নিত করা হয়।
| অ্যাকুইলা অপ্টিমাইজার× | পার্টিকেল সোয়ার্ম অপ্টিমাইজেশন (PSO)× | |
|---|---|---|
| ক্ষেত্র | অনুকূলকরণ | অনুকূলকরণ |
| পরিবার≠ | Machine learning | Process / pipeline |
| উদ্ভবের বছর≠ | 2021 | 1995 |
| প্রবর্তক≠ | Laith Abualigah | — |
| ধরন≠ | Nature-inspired metaheuristic algorithm | Population-based metaheuristic / swarm intelligence |
| মৌলিক উৎস≠ | Abualigah, L., Yousri, D., Abd Elaziz, M., Ewees, A. A., Al-qaness, M. A., & Gandomi, A. H. (2021). Aquila optimizer: A novel meta-heuristic optimization algorithm. Computers and Industrial Engineering, 157, 107250. DOI ↗ | Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948. DOI ↗ |
| অপর নাম≠ | AO | PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO) |
| সম্পর্কিত≠ | 3 | 6 |
| সারসংক্ষেপ≠ | The Aquila Optimizer (AO) is a nature-inspired metaheuristic algorithm presented by Abualigah et al. in 2021, modeled after the hunting behavior and sensory abilities of golden eagles (aquila chrysaetos). The algorithm captures the exploration and exploitation phases of eagle hunting, including high-altitude soaring, exploration with high-precision vision, and rapid diving attacks. AO is designed to solve both constrained and unconstrained optimization problems. | 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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