Simulated Annealing
Simulated annealing is a stochastic optimization technique used in computing science and operations research for finding an approximation to the global optimum of a given function. It is often used when the search space is discrete, or when the problem is too complex to be solved exactly in reasonable time.
Simulated annealing works in a similar way to the process of real-world annealing, applying heat to a material in order to break individual bonds and allow a new, more favorable distribution of atoms and molecules. In the digital world, simulated annealing reverses this process, starting from a randomly generated initial solution for the given problem and then slowly cooling the system, allowing it to seek out an optimal arrangement of elements that forms a stable and reliable result.
Starting with a given quantity of models, each of which is a potential solution, simulated annealing works by randomly choosing one of the models, then slightly modifying it and assessing how much of an improvement it represents in solving the problem. If the improvement is satisfactory, then the modified solution is accepted and used to find the next model; if it is not, then the attempt is discarded and another one is chosen. During this process, called a ‘temperature’ step, the parameters that define the process of modification and evaluation (such as the rate of change and the evaluation criteria) are adjusted such that a small number of large, significant improvements occur early on, and a larger number of smaller, aesthetic ones occur later. After each temperature step, the whole process is cooled slightly, meaning that future modifications have a lesser impact and become harder to come by, therefore slowing the process down and leading it to an increasingly stable solution.
Simulated annealing has many advantages over standard exhaustive search or gradient descent optimization. It works well with discontinuous or complex functions, for which uniform search may not result in optimal solutions. It is relatively slow, but guarantees a high degree of accuracy since it is a controlled, non-greedy technique. As there is not need to choose arbitrary starting points or predefined adjustment parameters, it is also scalable to large problems and can easily be applied in a distributed, multi-processor setting.
In conclusion, simulated annealing is a powerful tool for solving complex problems in computing science and operations research. As a stochastic technique, it is robust enough to handle large and difficult problems while being able to apply the same strategy to many different problem types. Its iterative nature allows it to slowly approach a more accurate and reliable model, and its scalability make it attractive for distributed applications.
