Simulated annealing algorithm

Scope

The simulated annealing algorithm is an optimization method that mimics the slow cooling of metals, which is characterized by a progressive reduction in the atomic movements that reduce the density of lattice defects until a lowest-energy state is reached [143]. Similarly, at each virtual annealing temperature, the simulated annealing algorithm generates a new potential solution (or neighbor of the current state) to the problem considered by altering the current state, according to a predefined criterion. The acceptance of the new state is then based on the satisfaction of the Metropolis criterion, and this procedure is iterated until convergence. The simulated annealing algorithm performs the following steps: The algorithm generates a random trial point. The algorithm chooses the distance of the trial point from the current point by a probability distribution with a scale depending on the current temperature.