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Simulated Annealing is a probabilistic optimization algorithm used to find the global minimum of a function in a large search space. It is inspired by the annealing process in metallurgy, where a material is heated to a high temperature and then gradually cooled to remove defects and optimize its structure. 

In optimization, Simulated Annealing starts with an initial solution and explores neighboring solutions by making random changes. The algorithm accepts new solutions even if they are worse than the current one with a certain probability. This probability decreases over time, mimicking the annealing process. 

The key idea is to escape local minima by allowing the algorithm to explore regions of the search space that might contain better solutions. Simulated Annealing is particularly useful for combinatorial optimization problems where the search space is discrete. The algorithm is characterized by the following steps: 

1. Initialize the system with a random solution. 

2. Define a temperature parameter that controls the likelihood of accepting worse solutions. 

3. Iterate through the following steps until the system reaches a low temperature: 

   a. Generate a neighboring solution. 

   b. Evaluate the cost (objective function) of the new solution. 

   c. If the new solution is better, accept it. If not, accept it with a probability determined by the temperature and the cost difference. 

   d. Reduce the temperature according to a cooling schedule. 

Simulated Annealing has been successfully applied to various optimization problems, including scheduling, traveling salesman problems, and more. 

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