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Line 49: − + − *Markov Chains − *Random Walk − *Global Optimization − *ParSA-->[2] − −
Temperature Scheduling in Simulated Annealing (view source)
Revision as of 06:58, 21 December 2007
, 06:58, 21 December 2007→Convergence and Prospective strategies
==Convergence and Prospective strategies==
==Convergence and Prospective strategies==
Convergence when spoken in the context of simulated annealing refers to how (if at all) a particular algorithm will approach the correct solution (or for very difficult problems a close to correct solution).
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>