knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-", out.width = "100%" )
DEoptimPIC provides some interesting tools and benchmark functions to test three different versions of the differential evolution algorithm. The Differential evolution algorithm is a nature inspired algorithm used for solving global optimization problems. Often it is also used for solving not complex constrained optimization problems. This package proposes the following DE versions:
You can install the development version from GitHub with:
# install.packages("devtools") devtools::install_github("FilippoPic/DEoptimPIC")
This is a basic example which shows you how to solve a common problem:
library(DEoptimPIC) l <- -500 u <- 500 n <- 10 NP <- 40 n_gen <- 400 f <- 0.9 CR <- 0.4 #to perform a standard DE run with strategy DE/rand/1 d <- DEbase(schwefel,l,u,n,NP,n_gen,f,CR,strategy = 1)
Now you can see the best value found by the algorithm:
d$f_best
Or see the convergence plot:
plot(d$f,type='l', col = 2) plot(log(d$f),type='l',col=2)
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