max_EI: max_EI: Maximization of the Constrained Expected Improvement...
In coldfir3/moko: Multi-Objective Kriging Optimization

Description Usage Arguments Value Examples

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

1 2	max_EI(model, lower = rep(0, model@d), upper = rep(1, model@d), control = NULL, optimcontrol = NULL)

`model`	An object of class `mkm`. This `model` must have a single objective (`model@m == 1`).
`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 `EI` function. One can control: `minimization` logical specifying if EI is used in minimiziation or in maximization (default: `TRUE`) `plugin` optional scalar, if not provided, the minimum (or maximum) of the current feasible observations. If there isn't any feasible design plugin is set to `NA` and the algorithm returns the value of the probabilty of constraints be met. `envir` optional enviroment specifying where to assign intermediate values. Default: `NULL`.
`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.

Vector. The best set of parameters found.

# --------------------------------------------
# Branin-Hoo function (with simple constraint)
# --------------------------------------------
n <- 10
d <- 2
doe <- replicate(d,sample(0:n,n))/n
fun_cost <- DiceKriging::branin
fun_cntr <- function(x) 0.2 - prod(x)
fun <- function(x) return(cbind(fun_cost(x),fun_cntr(x)))
res <- t(apply(doe, 1, fun))
model <- mkm(doe, res, modelcontrol = list(objective = 1, lower=c(0.1,0.1)))
max_EI(model)