Simulated annealing is an optimization method for finding the global optimum of a function. The algorithm is similar to a hill_climbing algorithm.
Simulated annealing, just like hill climbing, has one current solution. In each iteration, that solution takes a random step and either improves, stays the same, or becomes worse.
@article{yaltirakliwikisimulatedannealing, title = "Simulated annealing", author = "Gokberk Yaltirakli", journal = "gkbrk.com", year = "2021", url = "https://www.gkbrk.com/wiki/simulated-annealing/" }
Gokberk Yaltirakli, "Simulated annealing", May, 2021. [Online]. Available: https://www.gkbrk.com/wiki/simulated-annealing/. [Accessed May. 30, 2021].
Yaltirakli, G. (2021, May 30). Simulated annealing. https://www.gkbrk.com/wiki/simulated-annealing/
Gokberk Yaltirakli, Simulated annealing, GKBRK.COM (May. 30, 2021), https://www.gkbrk.com/wiki/simulated-annealing/