Simulated annealing is an optimization technique inspired by the process of annealing in metallurgy. In this process, materials are heated and then gradually cooled to remove defects and improve structure. Similarly, simulated annealing explores possible solutions to a problem by allowing random changes and gradually reducing the likelihood of accepting worse solutions as it "cools." This helps the algorithm escape local optima and find a more optimal solution. The method involves defining an objective function that measures the quality of solutions. Initially, the algorithm accepts both good and bad solutions, but as it progresses, it becomes more selective, focusing on refining the best solutions found. This balance between exploration and exploitation makes simulated annealing effective for complex optimization problems.