Salīdzināt metodes

	Simulated Annealing	Particle Swarm Optimization (PSO)
Nozare	Optimizācija	Optimizācija
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	1983	1995
Autors	—	—
Tips	Probabilistic metaheuristic / local search	Population-based metaheuristic / swarm intelligence
Pirmavots	Kirkpatrick, S., Gelatt, C.D. & Vecchi, M.P. (1983). Optimization by Simulated Annealing. Science, 220(4598), 671-680.	Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948.
Citi nosaukumi	Benzetimli Tavlama (Simulated Annealing), SA, probabilistic local search	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)
Saistītās	5	6
Kopsavilkums	Simulated annealing is a probabilistic local-search metaheuristic introduced by Kirkpatrick, Gelatt, and Vecchi in 1983. It models the physical annealing process in metallurgy — where a material is heated and then slowly cooled to reach a low-energy crystalline state — and uses this analogy to escape local optima in combinatorial and continuous optimization problems.	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.