SA2008 {LHD} | R Documentation |
Simulated Annealing for LHD with Multi-objective Optimization Approach
Description
SA2008
returns a n
by k
LHD matrix generated by simulated annealing algorithm with multi-objective optimization approach
Usage
SA2008(
n,
k,
N = 10,
T0 = 10,
rate = 0.1,
Tmin = 1,
Imax = 5,
OC = "phi_p",
p = 15,
q = 1,
maxtime = 5
)
Arguments
n |
A positive integer, which stands for the number of rows (or run size). |
k |
A positive integer, which stands for the number of columns (or factor size). |
N |
A positive integer, which stands for the number of iterations. The default is set to be 10. A large value of |
T0 |
A positive number, which stands for the user-defined initial temperature. The default is set to be 10. |
rate |
A positive percentage, which stands for temperature decrease rate, and it should be in (0,1). For example, rate=0.25 means the temperature decreases by 25% each time. The default is set to be 10%. |
Tmin |
A positive number, which stands for the minimium temperature allowed. When current temperature becomes smaller or equal to |
Imax |
A positive integer, which stands for the maximum perturbations the algorithm will try without improvements before temperature is reduced. The default is set to be 5. For the computation complexity consideration, |
OC |
An optimality criterion. The default setting is "phi_p", and it could be one of the following: "phi_p", "AvgAbsCor", "MaxAbsCor", "MaxProCriterion". |
p |
A positive integer, which is the parameter in the phi_p formula, and |
q |
The default is set to be 1, and it could be either 1 or 2. If |
maxtime |
A positive number, which indicates the expected maximum CPU time given by user, and it is measured by minutes. For example, maxtime=3.5 indicates the CPU time will be no greater than three and half minutes. The default is set to be 5. |
Value
If all inputs are logical, then the output will be a n
by k
LHD. This modified simulated annealing algorithm reduces columnwise correlations and maximizes minimum distance between design points simultaneously, with a cost of more computational complexity.
References
Joseph, V.R., and Hung, Y. (2008) Orthogonal-maximin Latin hypercube designs. Statistica Sinica, 18, 171-186.
Examples
#generate a 5 by 3 maximin distance LHD with the default setting
try=SA2008(n=5,k=3)
try
phi_p(try) #calculate the phi_p of "try".
#Another example
#generate a 8 by 4 nearly orthogonal LHD
try2=SA2008(n=8,k=4,OC="AvgAbsCor")
try2
AvgAbsCor(try2) #calculate the average absolute correlation.