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Temperature Scheduling in Simulated Annealing (view source)
Revision as of 20:28, 20 December 2007
, 20:28, 20 December 2007→Prospective strategies
The primary objective for potential cooling strategies lies in the determination of the α and ''K'' factors given in the ParSA section on improving solution quality in a lesser amount of time. By tracking both the chain length ''n'' and speed of convergence P(X<sub>n</sub> ∉ Cost<sub>min</sub>), one can find a linear plot relating all four of the quantities through the following relationship:
The primary objective for potential cooling strategies lies in the determination of the α and ''K'' factors given in the ParSA section on improving solution quality in a lesser amount of time. By tracking both the chain length ''n'' and speed of convergence P(X<sub>n</sub> ∉ Cost<sub>min</sub>), one can find a linear plot relating all four of the quantities through the following relationship:
<math>\ln{P(X_n \not\in Cost_{min})} = \alpha \ln{K} - \alpha \ln{n}</math>
<math>\ln{P(X_n \not\in Cost_{min})} = \alpha \ln{K} - \alpha \ln{n}</math>
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 efficient chain length to run multiple independent Markov chains.
==References==
==References==