Simulated annealing with noisy or imprecise energy measurements

Simulated Annealing for noisy cost functions

Energy Transforms for Metropolis and Simulated Annealing Algorithms Energy Transforms for Metropolis and Simulated Annealing Algorithms

We present here a method to speed up Metropolis algorithms which can be viewed as an alternative to simulated annealing. We show that this method of energy transformation compares favourably with simulated annealing in the asymptotic of a close to one probability of convergence. It requires an estimate of the minimum value of the energy function to be known, but if only a loose estimate is avai...

Fast Object Recognition in Noisy Images Using Simulated Annealing

A fast simulated annealing algorithm is developed for automatic object recognition. The object recognition problem is addressed as the problem of best describing a match between a hypothesized object and an image. The normalized correlation coefficient is used as a measure of the match. Templates are generated on-line during the search by transforming model images. Simulated annealing reduces t...

Robust optimization with simulated annealing

Complex systems can be optimized to improve the performance with respect to desired functionalities. An optimized solution, however, can become suboptimal or even infeasible, when errors in implementation or input data are encountered. We report on a robust simulated annealing algorithm that does not require any knowledge of the problems structure. This is necessary in many engineering applicat...

Distributed Simulated Annealing with MapReduce

Simulated annealing’s high computational intensity has stimulated researchers to experiment with various parallel and distributed simulated annealing algorithms for shared memory, message-passing, and hybrid-parallel platforms. MapReduce is an emerging distributed computing framework for large-scale data processing on clusters of commodity servers; to our knowledge, MapReduce has not been used ...