Simulated annealing (SA) is a probabilistic technique for approximating the global optimum of a given function. Specifically, it is a metaheuristic to approximate global optimization in a large search space for an optimization problem. For large numbers of local optima, SA can find the global optimum. It is often used when the search space is discrete (for example the traveling salesman problem, the boolean satisfiability problem, protein structure prediction, and job-shop scheduling). For problems where a fixed amount of computing resource is available, finding an approximate global optimum may be more relevant than attempting to find a precise local optimum. In such cases, SA may be preferable to exact algorithms such as gradient descent or branch and bound. The problems solved by SA are currently formulated by an objective function of many variables, subject to several mathematical constraints. In practice, a constraint violation can be penalized as part of the objective function. Similar techniques have been independently introduced on several occasions, including Pincus (1970), Khachaturyan et al. (1979, 1981), Kirkpatrick, Gelatt and Vecchi (1983), and Cerny (1985). In 1983, this approach was used by Kirkpatrick, Gelatt Jr., and Vecchi for a solution of the traveling salesman problem. They also proposed its current name, simulated annealing. The name of the algorithm comes from annealing in metallurgy, a technique involving heating and controlled cooling of a material to alter its physical properties. This notion of slow cooling implemented in the simulated annealing algorithm is interpreted as a slow decrease in the probability of accepting worse solutions as the solution space is explored. Accepting worse solutions allows for a more extensive search for the global optimal solution. Simulated annealing algorithms work by progressively decreasing the temperature from an initial positive value to zero. At each time step, the algorithm randomly selects a solution close to the current one, measures its quality, and moves to it according to the temperature-dependent probabilities of selecting better or worse solutions. The simulation can be performed either by a solution of kinetic equations for probability density functions, or by using a stochastic sampling method. The method is an adaptation of the Metropolis–Hastings algorithm, a Monte Carlo method to generate sample states of a thermodynamic system, published by N. Metropolis et al. in 1953.
Overview
The state s of some physical systems, and the function E(s) to be minimized, is analogous to the internal energy of the system in that state. The goal is to bring the system, from an arbitrary initial state, to a state with the minimum possible energy.
The basic iteration At each step, the simulated annealing heuristic considers some neighboring state s* of the current state s, and probabilistically decides between moving the system to state s* or staying in state s. These probabilities ultimately lead the system to move to states of lower energy. Typically, this step is repeated until the system reaches a state that is good enough for the application, or until a given computation budget has been exhausted.
The neighbors of a state Optimization of a solution involves evaluating the neighbor states, which are new states produced through conservatively altering the current state. For example, in the traveling salesman problem, each state is typically defined as a permutation of the cities to be visited, and the neighbors of any state are the set of permutations produced by swapping any two of these cities. The well-defined way in which the states are altered to produce neighboring states is called a move, and different moves give different sets of neighboring states. These moves usually result in minimal alterations of the current state, in an attempt to progressively improve the solution through iteratively improving its parts (such as the city connections in the traveling salesman problem). Simple heuristics like hill climbing, which move by finding better neighbor after better neighbor and stop when they have reached a solution which has no neighbors that are better solutions, is not guaranteed to lead to any of the existing better solutions – their outcome may easily be just a local optimum, while the actual best solution would be a global optimum that could be different. Metaheuristics use the neighbors of a solution as a way to explore the solution space, and although they prefer better neighbors, they probabilistically also accept worse neighbors to avoid getting stuck in local optima; they can find the global optimum if given enough time.
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