Сравнение на методи

	Оптимизация с изкуствен рояк от пчели (ABC)	Оптимизация чрез рояк от частици (PSO)
Област	Оптимизация	Оптимизация
Семейство	Process / pipeline	Process / pipeline
Година на възникване	2007	1995
Създател	Dervis Karaboga & Bahriye Basturk	—
Тип	Swarm Intelligence Metaheuristic	Population-based metaheuristic / swarm intelligence
Основополагащ източник	Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39(3), 459–471.	Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948.
Други названия	ABC Algorithm, Bee Colony Optimization, Swarm-Based Bee Search, Yapay Arı Kolonisi	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)
Свързани	3	6
Резюме	Artificial Bee Colony (ABC) is a population-based swarm intelligence metaheuristic introduced by Karaboga and Basturk in 2007. It models the cooperative foraging behavior of a honey bee colony to search for optimal solutions in continuous numerical optimization problems. The algorithm divides candidate solutions among three bee types — employed, onlooker, and scout — and iteratively refines them through local search and probabilistic selection, making it well-suited for researchers and engineers tackling complex, multimodal optimization landscapes.	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.