Optimal Latin hypercube designs for the Kullback- Leibler criterion. Advances in Statistical Analysis, 94(4), 341-351.Jourdan A, Franco J.Optimal Latin hypercube designs for the Kullback-Leibler criterion[J].Asta Advance Statistical Analysis, 2010, 94:341-351....
(Citation2014) were the first to use PSO for the construction of optimal designs and generated Latin-hypercube designs. Chen et al. (Citation2014) and Mak and Joseph (Citation2018) applied PSO to generate space-filling designs. PSO for generating optimal designs for non-linear models were ...
N., Hung, Y., Wang, W.: Optimizing latin hypercube designs by particle swarm. Stat. Comput. 23(5), 663–676 (2013). Article MathSciNet Google Scholar Cheng, R., Jin, Y.: A competitive swarm optimizer for large scale optimization. IEEE Trans. Cybern. 45(2), 191–204 (2015). ...
Commonly used grid generation methods include uniform grid, equispaced grid, Latin hypercube sampling, and so on. Pricing analysis of American options by generating a grid is also used in the finite difference method. Kwok et al36. gave an equispaced grid, $$\begin{aligned} \Delta y=1.5\...
In the literature, this issue is mainly solved by constructing efficient experimental designs. For instance, Quasi-Monte Carlo sampling was first used for computing sensitivity indices [13], [14]. The Latin hypercube design is popular for addressing the integral problem due to its one-dimensional ...
This is done for all LF’s, several load ramping directions (normalized vectors generated via Latin Hypercube sampling), and variable generation patterns (i.e.. variable generator costing parameters) resulting in an increased set of boundary data points. Intermediate solutions that satisfy N-1 ...
The Latin hypercube design (LHD), because of its one-dimensional projection uniformity, is commonly used in computer experiment. The randomly generated LHD may have too many concentrated design points, and factors may be highly correlated. In this article, we suggested a local greedy strategy for...
Latin hypercube designs (LHDs) have broad applications in constructing computer experiments and sampling for Monte-Carlo integration due to its nice property of having projections evenly distributed on the univariate distribution of each input variable. The LHDs have been combined with some commonly ...
Latin hypercube sampling ANN: Artificial neural network MSE: Mean square error NDS: Non-dominated sorting \(\pmb x\) : A vector of decision variables \(\pmb y\) : A vector of objective values X : A set of decision vector Y : A set of objective vector \(x_i\) : A ...
The divergence metrics were computed for replicated (n = 10) sample plans using the conditioned Latin hypercube sampling algorithm across increasing samples sizes of 10, 25, and 50 to 400 in steps of 50 to determine an optimal sample size; the sensitivity of the divergence metrics to increasing...