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LatinHypercubeSampling is a Julia package for the creation of optimised Latin Hypercube Sampling Plans. The genetic optimisation algorithm is largely based on the work by Bates et al. [1]. The package includes additional functionality for the creation of an optimised subset of an existing plan. For more details, see our paper.
Features:
- Creation of an optimised Latin Hypercube Sampling plan.
- Generate an optimised subset of an existing plan.
- Refine existing plan.
- Ability to include discrete parameters in the design.
The package is registered and can be installed with Pkg.add.
julia> Pkg.add("LatinHypercubeSampling")- STABLE — tagged version of the documentation.
- Magnus Urquhart - @MrUrq
Sampling the Rosenbrock function with an optimized Latin Hypercube sampling plan.
julia> plan, _ = LHCoptim(100,2,1000)
julia> scaled_plan = scaleLHC(plan,[(-5.0,5.0),(-5.0,5.0)])
julia> rosenbrock_2D(x) = (1.0 - x[1])^2 + 100.0 * (x[2] - x[1]^2)^2
julia> mapslices(rosenbrock_2D,scaled_plan; dims=2)Example of optimised LHC plan for 120 points in 2 dimensions.
[1]: Stuart Bates, Johann Sienz, and Vassili Toropov. "Formulation of the Optimal Latin Hypercube Design of Experiments Using a Permutation Genetic Algorithm", 45th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics & Materials Conference, Structures, Structural Dynamics, and Materials and Co-located Conferences, () https://doi.org/10.2514/6.2004-2011
@article{urquhart_surrogate-based_2020,
title = {Surrogate-based optimisation using adaptively scaled radial basis functions},
volume = {88},
issn = {1568-4946},
doi = {10.1016/j.asoc.2019.106050},
journal = {Applied Soft Computing},
author = {Urquhart, Magnus and Ljungskog, Emil and Sebben, Simone},
year = {2020},
}