Optimizers based on annealing mimic the thermodynamic process by which liquids freeze and metals anneal. Starting out at a high temperature, the atoms of a liquid or molten metal bounce rapidly about in a random fashion.
Slowly cooled, they mange themselves into an orderly configuration-a crystal-that represents a minimal energy state for the system.
Simulated in software, this thermodynamic process readily solves large-scale optimization problems.
As with genetic optimization, optimization by simulated annealing is a very powerful Stochastic technique, modeled upon a natural phenomenon, that can find globally optimal solutions and handle ill-behaved fitness functions.
Simulated annealing has effectively solved significant combinatorial problems, including the famous “traveling salesman problem,” and the problem of how best to arrange the millions of circuit elements found on modem integrated circuit chips, such as those that power computers. Methods based on simulated annealing should not be construed as limited to combinatorial optimization; they can readily be adapted to the optimization of real-valued parameters.
Consequently, optimizers based on simulated annealing are applicable to a wide variety of problems, including those faced by traders.
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