विधियों की तुलना करें

	जेनेटिक एल्गोरिथम	पार्टिकल स्वार्म ऑप्टिमाइज़ेशन (PSO)
क्षेत्र	अनुकूलन	अनुकूलन
परिवार	Process / pipeline	Process / pipeline
उद्भव वर्ष	1975	1995
प्रवर्तक	John Henry Holland	—
प्रकार	Population-based metaheuristic	Population-based metaheuristic / swarm intelligence
मौलिक स्रोत	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.
उपनाम	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)
संबंधित	5	6
सारांश	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.