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| Αλγόριθμος Honey Badger× | Βελτιστοποίηση Σμήνους Σωματιδίων (PSO)× | |
|---|---|---|
| Πεδίο | Βελτιστοποίηση | Βελτιστοποίηση |
| Οικογένεια≠ | Machine learning | Process / pipeline |
| Έτος προέλευσης≠ | 2023 | 1995 |
| Δημιουργός≠ | Fatma A. Hashim | — |
| Τύπος≠ | Nature-inspired metaheuristic algorithm | Population-based metaheuristic / swarm intelligence |
| Θεμελιώδης πηγή≠ | 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 ↗ |
| Εναλλακτικές ονομασίες≠ | HBA | PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO) |
| Συναφείς≠ | 5 | 6 |
| Σύνοψη≠ | 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. |
| ScholarGateΣύνολο δεδομένων ↗ |
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