Simulated annealing is a popular probabilistic optimization algorithm inspired by the physical process of annealing in metallurgy. In the context of artificial intelligence and machine learning, simulated annealing is used to find approximate solutions to complex optimization problems, especially when the search space is large and riddled with local optima.
The basic idea behind simulated annealing is to mimic how metals cool and settle into a minimum energy state. In metallurgy, when a material is heated and then slowly cooled, its particles arrange themselves into a highly ordered, low-energy structure. By analogy, simulated annealing starts with a high “temperature” that allows the algorithm to explore the solution space freely, even accepting worse solutions with a certain probability. As the temperature decreases, the algorithm becomes less likely to accept worse solutions. This gradual reduction in temperature is what helps the algorithm escape local minima and move toward a global optimum.
Here’s how it works: you start with an initial solution and a high temperature. At each iteration, you slightly modify the solution (this is called a “move”). If the new solution is better, it’s accepted. If it’s worse, it’s accepted with a probability that decreases as the temperature lowers. Over time, the temperature is “cooled”—usually by multiplying it by a cooling rate less than one. This means that as the search progresses, the algorithm becomes more conservative and focuses on improving the solution rather than exploring.
Simulated annealing is especially useful for combinatorial optimization problems where the number of possible solutions is immense and there’s no efficient way to check them all. Examples include scheduling, routing, feature selection, and neural network training. Its stochastic nature means it can escape local optima, a common pitfall for greedy algorithms and some deterministic optimization methods.
One of the key advantages of simulated annealing is its simplicity and flexibility. It doesn’t require derivative information, making it suitable for problems where gradients are hard to calculate or don’t even exist. However, the performance of simulated annealing depends heavily on the choice of parameters, such as the initial temperature, the cooling schedule, and the way new solutions are generated. Setting these parameters often requires experimentation and domain knowledge.
In summary, simulated annealing is a versatile optimization technique that leverages randomness to find good solutions to hard problems. It’s a foundational method in the family of metaheuristics and is often used as a baseline or in combination with other optimization algorithms in AI research and applications.
