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Line 59: − − [[Image:SA_perform.jpg|thumb|An example of convergence for a Simulated Annealing Run]] − − [[Image:SA_perform_2.jpg|thumb|Another example of convergence for a Simulated Annealing Run]] − − [[Image:SA_perform_3.jpg|thumb|Another example of convergence for a Simulated Annealing Run]]
Temperature Scheduling in Simulated Annealing (view source)
Revision as of 00:21, 21 December 2007
, 00:21, 21 December 2007→Convergence and Prospective strategies
==Convergence and Prospective strategies==
==Convergence and Prospective strategies==
[[Image:SA_perform.jpg|thumb|An example of convergence for a Simulated Annealing Run]]
[[Image:SA_perform_2.jpg|thumb|Another example of convergence for a Simulated Annealing Run]]
[[Image:SA_perform_3.jpg|thumb|Another example of convergence for a Simulated Annealing Run]]
*Markov Chains
*Markov Chains
*Random Walk
*Random Walk
Once a sufficient number of runs have been completed, the α and ''K'' factors will be known and can thereby be exploited to find the most effective chain length to run multiple independent Markov chains. Given the potential size of the search space, one can muse that the α factor will most likely be closer to one rather than to zero, because with the multiple run strategy, faster cooling will result in a particular chain settling very quickly to a minimum (which may be a local minimum). After settling, the cluster can then move on to a new chain to settle to another minima. If this is repeated, the chances of finding the global minima among one of the solutions is much greater than if only one chain were used.
Once a sufficient number of runs have been completed, the α and ''K'' factors will be known and can thereby be exploited to find the most effective chain length to run multiple independent Markov chains. Given the potential size of the search space, one can muse that the α factor will most likely be closer to one rather than to zero, because with the multiple run strategy, faster cooling will result in a particular chain settling very quickly to a minimum (which may be a local minimum). After settling, the cluster can then move on to a new chain to settle to another minima. If this is repeated, the chances of finding the global minima among one of the solutions is much greater than if only one chain were used.
==References==
==References==