algorithm - What does T represent in simulated Annealing? -

I want to use fake annealing for different situations in the net provides algorithm with every simulated annealing algorithm temperature example. . Like in the wiki

  s â ???? S0; EE (S) // Initial Position, Energy Sbest â ???? S; Ebest one ???? E initial "best" solution k ??? 0 / Energy Rating Counts while K & lt; Kmx and E & gt; Amex // time left & amp; Not quite good: T â ???? Temperature (k / km) // temperature calculation snap a ???? Neighbor (s) // Choose some neighbors Enew â ???? E (snove) / / Calculate your energy if P (E, NUU, T)> Random () so // should we go to it? S â ???? Snew; E Enew // Yes, change state. If the nave & lt; Ebest then // is this a new best? Sbest â ???? Snew; Ebest one ???? Save the 'New Neighbor' to Enew // 'Best Neighborhood' K Kashmir + 1 // Another evaluated return sbest / found / Return the best solution   
 Now this 'T' normal form What does that represent? Suppose I will use fake annealing in chess. I will use this algorithm to find the next step for the computer. I have current status (s) and its value is (e). I have next state (snoop) and their value (NEU), then what will be the 't' for chess? Do i need it Is there any normal form for this algorithm? I mean, without the example of this temperature where I can get the original idea! I can not find anyone, please help. Thanks in advance ......   
 
 
 All examples on the net use example examples because it is Standard Glossary for Fake Annealing - SA is a physics-driven technique, which has been done after an actual world event by the name of Annealing. This is very much the same as all the examples of genetic algorithms talk about genes and chromosomes.  
 If you are quite behind mathematics, there are some lucrative relationships between different customization meta-hysterics and some physical processes, usually being retrieved from the concept of entropy.  
 However, in very difficult words, temperature T in simulated annealing matches the desire or capability of the algorithm to go out of "minimum" for local discovery. A global (or at least, Better local) Minimum high temperatures conform to high randomness, jump around more, and can also end in worse configuration; Low temperatures are less consistent with low randomness (and ultimately purely greedy algorithms), and no one can escape the local minima, howsoever shallow it is.  
 How to use that idea for your applications, well, for the most metaherics to do the right things, there are some insights and some creativity because you can not always discuss the SA which is the temperature Does not talk about