# Differential Evolution Algorithms for Minimization Problems The general-purpose implementation of differential evolution algorithms for minimizing an user-defined objective function. ```r diffevo( objFunc, lower, upper, init = NULL, fixed = NULL, DE_INFO = NULL, seed = NULL, verbose = TRUE, ... ) ``` ## 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`. ## Returns 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. ## Examples ```r # 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 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 ``` ## 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.

diffevo function