max_EHVI: max_EHVI: Maximization of the Expected Hypervolume...
In moko: Multi-Objective Kriging Optimization

Description Usage Arguments Value Examples

Given an object of class mkm and a set of tuning parameters, max_EHVI performs the maximization of the Expected Hypervolume Improvement criterion and delivers the next point to be visited in an HEGO-like procedure.

max_EHVI(
  model,
  lower = rep(0, model@d),
  upper = rep(1, model@d),
  control = NULL,
  optimcontrol = NULL
)

`model`	An object of class `mkm`.
`lower`	Vector of lower bounds for the variables to be optimized over (default: 0 with length `model@d`),
`upper`	Vector of upper bounds for the variables to be optimized over (default: 1 with length `model@d`),
`control`	An optional list of control parameters, some of them passed to the `crit_EHI` function. One can control: `minimization` logical indicating if the EHVI is minimizing all objectives (`TRUE`, by default) or maximizing all objectives (`FALSE`). Mixed optimization is not currently accepted, if the user needs it, the cost functions should be modified prior Kriging modeling (i.e. inverting or multiplying the output by `-1`). `paretoFront` object of class `ps` containing the actual Pareto set. If not provided a Pareto set is built based on the current feasible observations (`model@response[model@feasible,]`). `nb.samp` number of random samples from the posterior distribution (with more than two objectives), default to 50, increasing gives more reliable results at the cost of longer computation time `seed` seed used for the random samples (with more than two objectives); `refPoint` reference point for Hypervolume Expected Improvement. If not provided, it is set to the maximum or minimum of each objective.
`optimcontrol`	Optional list of control parameters passed to the `GenSA` function. Please, note that the values are passed as the `control` parameter inside the `GenSA` function (`genSA(control = optimcontrol)`).

A list with components:

par: The best set of parameters found.
value: The value of expected hypervolume improvement at par.

# ------------------------
# The Nowacki Beam
# ------------------------
n <- 10
d <- 2
doe <- replicate(d,sample(0:n,n))/n
res <- t(apply(doe, 1, nowacki_beam, box = data.frame(b = c(10, 50), h = c(50, 250))))
model <- mkm(doe, res, modelcontrol = list(objective = 1:2, lower=c(0.1,0.1)))
max_EHVI(model)