Simulated Annealing Algorithm

In computer science, simulated annealing is a stochastic optimization algorithm designed to find approximate optimal solutions within large search spaces. Inspired by the metallurgical process where controlled heating and cooling reduces material energy to achieve stronger, more stable states, this algorithm employs a "temperature" parameter to probabilistically escape local optima during the search process. While not guaranteed to find global optima, it typically converges to high-quality solutions within acceptable timeframes. Implementation typically involves defining an energy function (objective function), neighbor generation mechanism, cooling schedule, and acceptance probability using the Metropolis criterion: P = exp(-ΔE/T) where ΔE represents energy change and T is the current temperature. The algorithm's key advantage lies in its balance between exploration (high-temperature phase) and exploitation (low-temperature phase), making it particularly effective for combinatorial optimization problems like traveling salesman or scheduling tasks.