HEGO: Efficient Global Optimization Algorithm based on the Hypervolume criteria
Description
Executes nsteps
iterations of the HEGO method to an object of class
mkm
. At each step, a kriging model is reestimated (including
covariance parameters reestimation) based on the initial design points plus
the points visited during all previous iterations; then a new point is
obtained by maximizing the Expected Hypervolume Improvement criterion (EHVI).
Usage
1 2 
Arguments
model 
An object of class 
fun 
The multiobjective and constraint cost function to be optimized.
This function must return a vector with the size of 
nsteps 
An integer representing the desired number of iterations, 
lower 
Vector of lower bounds for the variables to be optimized over
(default: 0 with length 
upper 
Vector of upper bounds for the variables to be optimized over
(default: 1 with length 
quiet 
Logical indicating the verbosity of the routine, 
control 
An optional list of control parameters, some of them passed to
the

optimcontrol 
Optional list of control parameters passed to the

Value
updated mkm
model
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13  # 
# The Nowacki Beam
# 
n < 20
d < 2
nsteps < 1 # value has been set to 1 to save compliation time, change this value to 40.
fun < nowacki_beam
doe < replicate(d,sample(0:n,n))/n
res < t(apply(doe, 1, fun))
model < mkm(doe, res, modelcontrol = list(objective = 1:2, lower = rep(0.1,d)))
model < HEGO(model, fun, nsteps, quiet = FALSE)
plot(nowacki_beam_tps$set)
points(ps(model@response[which(model@feasible),model@objective])$set, col = 'green', pch = 19)
