A simulated annealing algorithm is a stochastic optimization algorithm that is used to find the global optimum or near-optimum solution for a given problem, particularly in the cases where the search space is large and the optimization function is non-linear and non-convex. It is inspired by the physical process of annealing, where a material is heated and then gradually cooled to improve its structure and properties. In a simulated annealing algorithm, the search process starts with a random or initial solution, and then iteratively moves towards better solutions. The algorithm accepts solutions that may not always be optimal, allowing it to escape from local minima and explore the solution space thoroughly. The algorithm uses an acceptance criterion that is based on the probability of accepting the new solution, which is annealed over time by gradually reducing the temperature of the system. The cooling schedule is typically designed to balance between exploration and exploitation, allowing the algorithm to converge to a global optimum solution with high probability.
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