Confronta i metodi
Esamina i metodi selezionati fianco a fianco; le righe che differiscono sono evidenziate.
| Honey Badger Algorithm× | Ottimizzazione a Sciame di Particelle (PSO)× | |
|---|---|---|
| Campo | Ottimizzazione | Ottimizzazione |
| Famiglia≠ | Machine learning | Process / pipeline |
| Anno di origine≠ | 2023 | 1995 |
| Ideatore≠ | Fatma A. Hashim | — |
| Tipo≠ | Nature-inspired metaheuristic algorithm | Population-based metaheuristic / swarm intelligence |
| Fonte seminale≠ | 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 ↗ |
| Alias≠ | HBA | PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO) |
| Correlati≠ | 5 | 6 |
| Sintesi≠ | 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. |
| ScholarGateInsieme di dati ↗ |
|
|