DEinterface: Minimization by Differential Evolution

In martinzaefferer/COBBS: Continuous Optimization Benchmarks By Simulation

Minimization by Differential Evolution

Description

For minimization, this function uses the "DEoptim" method from the codeDEoptim package.

Usage

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

Arguments

x	optional start point, not used in DEoptim
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 is 200. populationSize Population size or number of particles in the population. Default is 10*dimension.
...	passed to fun

Value

list, with elements

x

archive of the best member at each iteration

y

archive of the best value of fn at each iteration

xbest

best solution

ybest

best observation

count

number of evaluations of fun

Examples

objfun <- function (x) matrix(apply(x, 1, function(x) sum(x^2)), , 1)
res <- DEinterface(,lower = c(-10,-20),upper=c(20,8),fun = objfun)
res$ybest

martinzaefferer/COBBS documentation built on July 19, 2023, 4:12 a.m.