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Simulated annealing is a mathematical and modeling method that is often used to help find a global optimization in a particular function or problem. Simulated annealing gets its name from the process of slowly cooling metal, applying this idea to the data domain.
Simulated annealing is also known simply as annealing.
Various uses of simulated annealing help to refine algorithms that are built toward modeling global optimizations or optimums. One example is cited in Wolfram MathWorld, where the "traveling salesman problem" is attacked with an algorithm that uses simulated annealing in order to break down optimal outcomes. WM suggests that simulated annealing uses two of what it calls "tricks" to more fully optimize results – the first one is allowing certain "bad trades" that open up greater efficiencies within their domains. The second one is described as "lowering the temperature" of the data construct by slowly limiting the size of allowed bad trades.
Processes like simulated annealing are used to build more sophisticated operations that, while working on more complicated sets of rules, develop greater efficiencies related to their goals.