השוואת שיטות

	אלגוריתם דבשן הדלעות	אופטימיזציית נחיל חלקיקים (PSO)
תחום	אופטימיזציה	אופטימיזציה
משפחה	Machine learning	Process / pipeline
שנת המקור	2023	1995
הוגה השיטה	Fatma A. Hashim	—
סוג	Nature-inspired metaheuristic algorithm	Population-based metaheuristic / swarm intelligence
מקור מכונן	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.	Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948.
כינויים	HBA	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)
קשורות	5	6
תקציר	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.