Simulated Annealing
Simulated Annealing is a probabilistic optimization algorithm inspired by the annealing process in metallurgy, where materials are heated and slowly cooled to reduce defects and achieve a low-energy state. It is used to approximate the global optimum of a given function in a large search space, particularly for complex problems where traditional methods like gradient descent may get stuck in local minima. The algorithm explores solutions by accepting worse moves with a probability that decreases over time, allowing it to escape local optima and converge toward a global solution.
Developers should learn Simulated Annealing when tackling NP-hard optimization problems, such as the traveling salesman problem, scheduling, or resource allocation, where exact solutions are computationally infeasible. It is especially useful in scenarios with rugged search spaces, as its stochastic nature helps avoid premature convergence to suboptimal solutions. This makes it a valuable tool in fields like operations research, machine learning hyperparameter tuning, and engineering design optimization.