What is simulated annealing?
simulated annealing is a computer technique that can find good - though not necessarily optimal - solving the problem. This is so named because it mimics the metallurgical process of annealing. In metals, annealing is a process of cleaning by heating metal and then slowly cooling it. The computer program "cleans" the solution space until only a solution that is the best or almost almost the best.
There are two critical factors that a user of a simulated annealing program must specify: the initial temperature or a percentage of worse solutions that can be explored; and the cooling rate, which is the rate at which this percentage will decrease. The low initial temperature often ends up with the result far from optimal. Starting with a very high temperature, it may lead to a lot more time to search than necessary. Similarly, it will be too high cooling to bring bad results, while a very low cooling rate will result in a program that has been running for a very long time.
The "high temperature" state for a simulated brindle program is a setting that allows him to look at a wide range of solutions, including many that are worse than the solution she has already found. The computer can look at many solutions that are worse than the current solutions to avoid sticking to the local minimum, which is much worse than the best. As an example, it is that it starts at the top of the hill or mountain to reach the base. There may be inlets or kostiny along the way. If the computer can't go uphill far enough to get out, get stuck, even if it's not near the base.
How far the program can go through is determined by the percentage of worse solutions that the program can explore. Over time, a better solution has been found and the risk of deepchasm is reduced, so the percentage of worse solutions that the computer can explore is reduced. The reduction of this fraction is referred to as "cooling". When the temperature reaches overDE set fraction-which may not be 0-converted search.
The reason for using simulated annealing or other artificial intelligence search techniques is shortening to a manageable amount needed to find an almost optimal solution. For many problems, exhaustive search - testing every possible solution against each other - last for months or years. Genetic algorithms are the best known alternative to simulated annealing. Other popular algorithms search for artificial intelligence include Ant Colony optimization, particle swarm optimization, closest neighbor and Bayesian classifiers.