Nothing
#
# Test the optimization process
#
context("success")
fonseca <- function(i) {
s2 <- 1 / sqrt(2)
val1 <- 1 - exp(-(x[i,1] - s2) * (x[i,1] - s2) - (x[i,2] - s2) * (x[i,2] - s2))
s2 <- 1 / sqrt(2)
val2 <- 1 - exp(-(x[i,1] + s2) * (x[i,1] + s2) - (x[i,2] + s2) * (x[i,2] + s2))
return(c(val1, val2))
}
nvar <- 2 # number of variables
bounds <- matrix(data = 1, nrow = nvar, ncol = 2) # upper and lower bounds
bounds[, 1] <- -4 * bounds[, 1]
bounds[, 2] <- 4 * bounds[, 2]
nobj <- 2 # number of objectives
minmax <- c(FALSE, FALSE) # min and min
popsize <- 100 # size of the genetic population
archsize <- 10 # size of the archive for the Pareto front
maxrun <- 100 # maximum number of calls
prec <- matrix(1.e-3, nrow = 1, ncol = nobj) # accuracy for the convergence phase
test_that("Optimization process is OK", {
# flag must be TRUE
results <-
caRamel(nobj,
nvar,
minmax,
bounds,
fonseca,
popsize,
archsize,
maxrun,
prec,
carallel=FALSE)
expect_true(results$success==TRUE)
})
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