Description: Simulated annealing is a probabilistic technique used to find the global minimum of a given function, especially in the context of optimization problems. Inspired by the physical process of annealing in metallurgy, where a material is heated and cooled to remove defects, simulated annealing applies a similar approach in the computational realm. This technique is based on the idea of efficiently exploring the solution space, allowing the system to escape local minima by accepting worse solutions with a certain probability. This probability decreases as the process progresses, enabling convergence towards the global minimum. Simulated annealing is particularly valuable in problems where the search space is vast and complex, as its stochastic nature allows it to avoid getting trapped in suboptimal solutions. The technique has been adapted in various fields, including artificial intelligence and optimization in complex systems, and is integral to many optimization algorithms.
History: Simulated annealing was first introduced in 1983 by S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi in a paper proposing this technique as a method for solving combinatorial optimization problems. Since then, it has evolved and been adapted to various fields, including artificial intelligence and optimization in complex systems.
Uses: Simulated annealing is used in a variety of applications, including route optimization, circuit design, scheduling, and resource allocation. It is also applied in machine learning problems where cost functions need to be minimized.
Examples: A practical example of simulated annealing is its application in vehicle routing optimization, where the shortest route is sought to minimize time and costs. Another example is its use in optimizing parameters in machine learning models, where hyperparameters are adjusted to improve model performance.
