Simulated Annealing
Simulated Annealing is a metaheuristic optimization algorithm that is used to find the global optimum in a large search space. It is inspired by the annealing process in metallurgy, where a metal is heated and then slowly cooled to reach a low-energy crystalline state. Similarly, in simulated annealing, a system starts with a high-energy state and then gradually cools down to a low-energy state by accepting or rejecting new solutions based on a probability function. This probability function allows the algorithm to escape local optima and explore the search space more effectively. Simulated Annealing has been successfully applied to a wide range of optimization problems, including combinatorial optimization, machine learning, and neural network training.
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