optimLHD: Minimization by Latin Hypercube Sampling

In SPOT: Sequential Parameter Optimization Toolbox

Minimization by Latin Hypercube Sampling

Description

This uses Latin Hypercube Sampling (LHS) to optimize a specified target function. A Latin Hypercube Design (LHD) is created with designLHD, then evaluated by the objective function. All results are reported, including the best (minimal) objective value, and corresponding design point.

Usage

optimLHD(x = NULL, fun, lower, upper, control = list(), ...)

Arguments

x	optional matrix of points to be included in the evaluation
fun	objective function, which receives a matrix x and returns observations y
lower	boundary of the search space
upper	boundary of the search space
control	list of control parameters funEvals Budget, number of function evaluations allowed. Default: 100. retries Number of retries for design generation, used by designLHD. Default: 100.
...	passed to fun

Value

list, with elements

x

archive of evaluated solutions

y

archive of observations

xbest

best solution

ybest

best observation

count

number of evaluations of fun

message

success message

Examples

res <- optimLHD(,fun = funSphere,lower = c(-10,-20),upper=c(20,8))
res$ybest

SPOT documentation built on June 26, 2022, 1:06 a.m.