AN IMPROVED PERTURBATION MECHANISM FOR SIMULATED ANNEALING SIMULATION

Simulated annealing (SA) is being increasingly used for the generation of stochastic models of spatial phenomena because of its flexibility to integrate data of diverse types and scales. The major shortcoming o f SA is the extensive CPU requirements. We present a perturbation mech anism that significantly improves the CPU speed. Two conventional pert urbation mechanisms are to (1) randomly select two locations and swap their attribute values, or (2) visit a randomly selected location and draw a new value from the global histogram. The proposed perturbation mechanism is a modification of option 2: each candidate value is drawn from a local conditional distribution built with a template of krigin g weights rather than from the global distribution. This results in ac cepting more perturbations and in perturbations that improve the vario gram reproduction for short scale lags. We document the new method, th e increased convergence speed, and the improved variogram reproduction . Implementation details of the method such as the size of the local n eighborhood are considered.