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
In Field
Technique Used By
Source Quantum Annealing in the Transverse Ising Model (arXiv)
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.
In Group: Optimization and Machine Learning Algorithms, Lead
Simulated annealing (SA) is a probabilistic technique for approximating the global optimum of a given function.
View the Source Quantum Annealing in the Transverse Ising Model (arXiv)
Technique Used By: Quantum annealing, AbstractQuote, Technique Used By: Quantum annealing, Abstract
We introduce quantum fluctuations into the simulated annealing process of optimization problems
Reader Challenges (0)
No disputes yet. Spotted an error or a better source? Open the first one.
Sign in to dispute this or suggest a correction.