Process / pipeline
Simulated Annealing — Probabilistic Optimization
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.
Open in MethodMindSoonVideoSoon
Read the full method
Members only
Sign inSign in with a free account to read this section.
Sources
- Kirkpatrick, S., Gelatt, C.D. & Vecchi, M.P. (1983). Optimization by Simulated Annealing. Science, 220(4598), 671-680. DOI: 10.1126/science.220.4598.671 ↗
- van Laarhoven, P.J.M. & Aarts, E.H.L. (1987). Simulated Annealing: Theory and Applications. Springer. ISBN: 9789027725431
Related methods
Referenced by
Ant Colony OptimizationBayesian Simulated AnnealingBayesian Tabu SearchCuckoo SearchDeterministic Genetic AlgorithmDeterministic Particle Swarm OptimizationDeterministic Simulated AnnealingGenetic AlgorithmGrey Wolf OptimizerHarmony SearchMulti-objective simulated annealingParticle Swarm OptimizationRobust Simulated AnnealingStochastic Genetic AlgorithmStochastic Tabu SearchTabu SearchVariable Neighborhood SearchWhale Optimization Algorithm