Latin Hypercube Designs (LHDs) Algorithms
Contains functions for finding space-filling Latin Hypercube Designs (LHDs), e.g. maximin distance LHDs.
Unlike other packages, our package is particularly useful in the area of Design and Analysis of Experiments (DAE). More
specifically, it is very useful in design of computer experiments. One advantage of our package is its comprehensiveness.
It contains a variety of heuristic algorithms (and their modifications) for searching maximin distance LHDs. In
addition to that, it also contains other useful tools for developing and constructing maximin distance LHDs.
In the future, algebraic construction methods will be added. Please refer to the function documentations for the detailed
references of each function. Among all the references we used, one reference should be highlighted here, which
is Ruichen Jin, Wei Chen, Agus Sudjianto (2005) <doi:10.1016/j.jspi.2004.02.014>. They provided a new form of phi_p
criterion, which does not lose the space-filling property and simultaneously reduces the computational complexity when
evaluating (or re-evaluating) an LHD. Their new phi_p criterion is a fundamental component of our many functions. Besides, the
computation nature of the new phi_p criterion enables our functions to have less CPU time.
Functions in LHD
|dij||Calculate the Inter-site Distance|
|SA2008||Simulated Annealing for LHD with Multi-objective Optimization Approach|
|OA2LHD||Transfer an Orthogonal Array (OA) into a LHD|
|phi_p||Calculate the phi_p Criterion|
|SLHD||Sliced Latin Hypercube Design (SLHD)|
|exchange||Exchange two random elements|
|OASA||Orthogonal-Array-Based Simulated Annealing|
|GA||Genetic Algorithm for LHD|
|SA||Simulated Annealing for LHD|
|rLHD||Generate a random Latin Hypercube Design (LHD)|
|LaPSO||Particle Swarm Optimization for LHD|
Vignettes of LHD
Last month downloads
|License||MIT + file LICENSE|
|Packaged||2019-10-06 15:04:56 UTC; Hongzhi Wang|
|Date/Publication||2019-10-07 20:00:02 UTC|
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