Abstract
Simulated annealing is a metaheuristic commonly used for combinatorial optimization in many industrial applications. Its runtime behavior is controlled by an algorithmic component known as the annealing schedule. The classic annealing schedules have control parameters that must be set o
Highlights
Simulated Annealing (SA), introduced by Kirkpatrick et al (1983), is a metaheuristic commonly used for combinatorial optimization in many industrial applications (Delahaye et al, 2019)
SA begins with a random solution to a combinatorial optimization problem, and is initialized with a high value of a control parameter referred to as “temperature.” This temperature is adjusted during the run by a component of the SA called the annealing schedule
We presented an optimized version of an existing adaptive annealing schedule for SA, known as the Modified Lam
Summary
Simulated Annealing (SA), introduced by Kirkpatrick et al (1983), is a metaheuristic commonly used for combinatorial optimization in many industrial applications (Delahaye et al, 2019). If their value on the optimization objective is not good This is similar to the physical annealing process where at high temperatures one can change the shape of the metal or glass . Later in the run of SA, when the temperature is lower, SA is less likely to accept a neighbor if the current solution is better, settling in upon a locally optimal solution. This is similar to the physical process where internal stresses are minimized in the final product resulting in a stable form.
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