Simulated annealing is an optimization method for finding the global optimum of a function. The algorithm is similar to a Hill climbing algorithm.
Comparison with Hill Climbing
Simulated annealing, just like hill climbing, keeps track of one current solution. In each iteration, that solution takes a random step and either improves, stays the same, or becomes worse.
Useful links and resources
Tags: Numerical optimization Algorithm
Citation
If you find this work useful, please cite it as:
@article{yaltirakli,
title = "Simulated annealing",
author = "Yaltirakli, Gokberk",
journal = "gkbrk.com",
year = "2025",
url = "https://www.gkbrk.com/simulated-annealing"
}
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IEEE Citation Gokberk Yaltirakli, "Simulated annealing", February, 2025. [Online]. Available: https://www.gkbrk.com/simulated-annealing. [Accessed Feb. 09, 2025].
APA Style Yaltirakli, G. (2025, February 09). Simulated annealing. https://www.gkbrk.com/simulated-annealing
Bluebook Style Gokberk Yaltirakli, Simulated annealing, GKBRK.COM (Feb. 09, 2025), https://www.gkbrk.com/simulated-annealing