A simulated annealing-based algorithm for selecting balanced samples

Abstract Balanced sampling is a random method for sample selection, the use of which preferable when auxiliary information available all units population. However, implementing balanced can be challenging task, and this due in part to computational efforts required necessity respect balancing constraints inclusion probabilities. In present paper, new algorithm selecting samples proposed. This inspired by simulated annealing algorithms, as selection interpreted an optimization problem. A set simulation experiments example using real data shows efficiency accuracy proposed algorithm.