Simulated annealing (SA) is a generic probabilistic metaheuristic for the global optimization problem of applied mathematics, namely locating a good approximation to the global minimum of a given function in a large search space. It is often used when the search space is discrete (e.g., all tours that visit a given set of cities). For certain problems, simulated annealing may be more effective than exhaustive enumeration -- provided that the goal is merely to find an acceptably good solution in a fixed amount of time, rather than the best possible solution.
Each step of the SA algorithm replaces the current solution by a random "nearby" solution, chosen with a probability that depends on the difference between the corresponding function values and on a global parameter T (called the temperature), that is gradually decreased during the process. The dependency is such that the current solution changes almost randomly when T is large, but increasingly "downhill" as T goes to zero. The allowance for "uphill" moves saves the method from becoming stuck at local minima—which are the bane of greedier methods.
Graphical Ontology Browser
- Click on a node to jump to the content of that node
- Pan to see the rest of the graph
- Scroll the mousewheel up and down to zoom in and out
- Rearrange the nodes in the graph by dragging a node to a different position