Сравнение на методи

	Генетичен алгоритъм	Оптимизация чрез рояк от частици (PSO)
Област	Оптимизация	Оптимизация
Семейство	Process / pipeline	Process / pipeline
Година на възникване	1975	1995
Създател	John Henry Holland	—
Тип	Population-based metaheuristic	Population-based metaheuristic / swarm intelligence
Основополагащ източник	Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.	Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948.
Други названия	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)
Свързани	5	6
Резюме	A genetic algorithm (GA) is a population-based metaheuristic optimization method introduced by John Henry Holland (1975) that mimics the principles of natural selection. It maintains a population of candidate solutions and iteratively improves them through selection, crossover, and mutation operators, making it especially powerful on discontinuous, non-convex, and multi-modal search spaces where classical gradient-based methods fail.	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.