This paper presents the development of a hybrid fuzzy modeling method for short-term load forecasting. The new approach employs the orthogonal least squares method to create the fuzzy model and a constrained optimization algorithm to perform the parameter learning. The proposed model is tested...

This paper presents a comparative study of four optimisation algorithms based on simulated annealing: the Gibbs Sampler, the Metropolis algorithm, the Iterated Conditional Modes, and an original method of random descent proposed by the authors. Comparison criteria that have been chosen are the...

In this paper, we propose a framework for selecting a high quality global optimal solution for discrete stochastic optimization problems with a predetermined confidence level using general random search methods. This procedure is based on performing the random search algorithm several...

Testing integrated circuits is a costly process. The present article investigates combinatorial optimization problems reducing subsets of tests, which are redundant in a statistical sense. The solution to those problems is brought about by application of the Simulated annealing local search...

The aim of this study is to demonstrate by means of numerical experiments that the simulation of random fields may provide images that are too spatially disorganized to represent correctly some continuous properties of geological reservoirs. Increasing the number of conditioning data does not...