विधियों की तुलना करें

	पार्टिकल स्वार्म ऑप्टिमाइज़ेशन (PSO)	जेनेटिक एल्गोरिथम
क्षेत्र	अनुकूलन	अनुकूलन
परिवार	Process / pipeline	Process / pipeline
उद्भव वर्ष	1995	1975
प्रवर्तक	—	John Henry Holland
प्रकार	Population-based metaheuristic / swarm intelligence	Population-based metaheuristic
मौलिक स्रोत	Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948.	Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.
उपनाम	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon
संबंधित	6	5
सारांश	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.	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.