A probabilistic optimization algorithm, inspired by the metallurgical process of annealing, that occasionally accepts a worse candidate solution in order to escape a local optimum, with the willingness to accept worse solutions decreasing over time as the algorithm cools toward a final answer.
Facts
Origin YearSimilar techniques were introduced independently earlier, including by Pincus in 1970; the 1983 Kirkpatrick, Gelatt and Vecchi paper gave the technique its current name and popularized it. Core PrincipleA probabilistic optimization technique that approximates the global optimum of a function by allowing occasional moves to worse solutions, with the probability of accepting a worse move decreasing over the run, so the search explores broadly at first and settles as it proceeds. 1 Connections
Sources
1. Wikipedia: Simulated annealing
Wikimedia FoundationLead section, history paragraph
In 1983, this approach was used by Kirkpatrick, Gelatt Jr., and Vecchi for a solution of the traveling salesman problem. They also proposed its current name, simulated annealing.
Lead section, first paragraph
Simulated annealing (SA) is a probabilistic technique for approximating the global optimum of a given function.
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