Simulated annealing is an optimization algorithm inspired by metallurgy. It combines Markov chain Monte Carlo and the Metropolis algorithm to find optimal solutions to problems by mimicking the physical process of annealing. The algorithm starts at a high temperature and progressively lowers it, simulating the cooling of metals in annealing. This allows the algorithm to avoid being trapped in local minima and find the global minimum, allowing for problems to be solved that cannot be solved using traditional optimization techniques alone.