globpso (version 1.3.1)

diffevo: Differential Evolution Algorithms for Minimization Problems

Description

The general-purpose implementation of differential evolution algorithms for minimizing an user-defined objective function.

Usage

diffevo(
  objFunc,
  lower,
  upper,
  init = NULL,
  fixed = NULL,
  DE_INFO = NULL,
  seed = NULL,
  verbose = TRUE,
  ...
)

Value

An List.

par

the global best particle.

val

the objective function value of the global best particle.

history

a vector of objective function values of the global best particle in DE search history.

cputime

the computational time in seconds.

Arguments

objFunc

The R or Rcpp compiled objective function. See the example.

lower

The vector of finite lower bounds of the search domain. Its length should be equal to the dimension of the domain space.

upper

The vector of finite upper bounds of the search domain. Its length should be equal to the dimension of the domain space.

init

The vector of initial population. Its length should be equal to the dimension of the domain space. When there are more than one initial vectors, specify init as a matrix. Each row vector represents one initial point. The default for init is NULL.

fixed

The vector of real values and NA values that controls DE to search only for the NA-valued components.

DE_INFO

The list of DE parameters generated by getDEInfo().

seed

The random seed that controls initial population of DE. The default is NULL.

verbose

The logical value controls if DE would reports the updating progress. The default is TRUE.

...

Further arguments to be passed to objFunc.

References

Storn, R., & Price, K. (1997). Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 11, 341-359.

Examples

# three-dimensional function
objf <- function(x, loc) {
  val <- 0
  for (i in 1:length(x)) {
    val <- val + (x[i] - loc)^2
  }
  return(val)
}

upp_bound <- rep(5, 3)
low_bound <- rep(-5, 3)
loc_shift <- 1

alg_setting <- getDEInfo(nPop = 32, maxIter = 100, deType = "rand-1", 
                         sf = 0.5, cr = 0.1)
res <- diffevo(objFunc = objf, lower = low_bound, upper = upp_bound, 
               DE_INFO = alg_setting, loc = loc_shift)
res$par
res$val

# C++ function example
# \donttest{
library(Rcpp)
library(RcppArmadillo)
objf_c <- cppFunction('double objf_c(SEXP x, SEXP loc) {
    double val = 0;
    double loc_c = (double)Rcpp::as(loc);
   arma::rowvec x_c = (arma::rowvec)Rcpp::as(x);
   for (arma::uword i = 0; i < x_c.n_elem; i++) {
     val += (x_c(i) - loc_c)*(x_c(i) - loc_c);
   }
    return val;
 }', depends = "RcppArmadillo")

alg_setting <- getDEInfo(nPop = 32, maxIter = 100, deType = "rand-1", 
                         sf = 0.5, cr = 0.1)
res_c <- diffevo(objFunc = objf_c, lower = low_bound, upper = upp_bound, 
                       DE_INFO = alg_setting, loc = 1)
res_c$par
res_c$val
# }