README.md

In FilippoPic/DEoptimPIC: DEoptimPIC provides some interesting tools and benchmark functions to test three different versions of the differential evolution algorithm

DEoptimPIC

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:

• BaseDE: the standard DE algorithm for solving optimization problems, provided with 4 mutation strategies
• MDE: a modified version of the DE that automatically select a strategy from a pool of 2 with different probabilities
• SAmDE: a modified version of the DE that selects, based on a probability, the mutation strategy from a pool of 4 strategies

Installation

You can install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("FilippoPic/DEoptimPIC")

Example

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
#> [1] 0.003388758

Or see the convergence plot:

plot(d$f,type='l', col = 2)

plot(log(d$f),type='l',col=2)

FilippoPic/DEoptimPIC documentation built on Feb. 14, 2022, 5:12 a.m.