The hydrophobic-polar model has been widely studied in the field of protein structure prediction both for theoretical purposes and as a benchmark for new optimization strategies. In this work we introduce a new heuristics based on Ant Colony Optimization and Markov Chain Monte Carlo that we called Hybrid Monte Carlo Ant Colony Optimization. We describe this method and compare results obtained o...

Efficient generation of scenarios is a central problem in evaluating the expected value of a random function in the stochastic optimization. We study the use of sparse grid scenario generation method for this purpose. We show that this method is uniformly convergent, hence, also epi-convergent. We numerically compare the performance of the sparse grid method with several Quasi Monte Carlo (QMC)...

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Accurate estimation of state variables and model parameters is essential for efficient process operation. The Bayesian formulation of the estimation problem suggests a general solution for nonlinear systems. However, a practically feasible implementation of the solution has not been available until recently. Most existing methods have had to rely on simplifying assumptions to obtain an approxim...

5 1 Overview of the Thesis 7 2 Global Optimization 11 2.1 The Global Optimization Problem . . . . . . . . . . . 12 2.2 Global Optimization Methods . . . . . . . . . . . . . . 14 2.3 Selected Optimization Methods . . . . . . . . . . . . . 17 2.3.1 Monte Carlo . . . . . . . . . . . . . . . . . . . 18 2.3.2 Hill Climbing . . . . . . . . . . . . . . . . . . . 19 2.3.3 Simulated Annealing . . . . . ...

This paper investigates worst-case analysis of a moving obstacle avoidance algorithm for unmanned vehicles in a dynamic environment in the presence of uncertainties and variations. Automatic worst-case search algorithms are developed based on optimization techniques, illustrated by a Pioneer robot with a moving obstacle avoidance algorithm developed using the potential field method. The uncerta...

Suppose that it is desired to estimate certain parameters associated with a model of an object that is contained within a larger scene and that only indirect measurements of the scene are available. The optimal solution is provided by a Bayesian approach, which is founded on the posterior probability density distribution. The complete Bayesian procedure requires an integration of the posterior ...

We propose a variant of the Simulated Annealing method for optimization in the multivariate analysis of diierentiable functions. The method uses the Hybrid Monte Carlo algorithm for the proposal of new conngurations. We show how this choice can improve the performance of simulated annealing methods by allowing much faster annealing schedules.

A method for sequence optimization in protein models is presented. The approach, which has inherited its basic philosophy from recent work by Deutsch and Kurosky @Phys. Rev. Lett. 76, 323 ~1996!# by maximizing conditional probabilities rather than minimizing energy functions, is based upon a different and very efficient multisequence Monte Carlo scheme. By construction, the method ensures that ...