Updating the package...

.Rbuildignore

@@ -1,12 +1,15 @@
-src/
 \.pdf$
 \.Rproj.*
 \.Rhist.*
 R/abc\.R
 ^.*\.Rproj$
 ^\.Rproj\.user$
 README\.md
+README\.Rmd
+^playground$
 LICENSE
 \.git/
 source/
 .*\.sh
+^appveyor\.yml$
+^\.travis\.yml$

.travis.yml

@@ -0,0 +1,5 @@
+# R for travis: see documentation at https://docs.travis-ci.com/user/languages/r
+
+language: R
+sudo: false
+cache: packages

ChangeLog

@@ -0,0 +1,4 @@
+2016-11-03  George G. Vega Yon (g.vegayon@gmail.com)
+	* R/*.R: Adding roxygen to the project.
+	* src/abc.cpp: Adding pure C++ version of the function
+	* playground/*: Cleaning up

DESCRIPTION

@@ -1,14 +1,22 @@
 Package: ABCoptim
 Type: Package
 Title: Implementation of Artificial Bee Colony (ABC) Optimization
-Version: 0.13.11
-Date: 2013-11-05
-Author: George Vega Yon <g.vegayon@gmail.com> [aut], Enyelbert Muñoz <enyeldoc2011@gmail.com> [ctb]
-Maintainer: George Vega Yon <g.vegayon@gmail.com>
-Description: An implementation of Karaboga (2005) Artificial Bee Colony Optimization algorithm. This (working) version is a Work-in-progress, which is why it has been implemented using pure R code. This was developed upon the basic version programmed in C and distributed at the algorithm's official website.
+Version: 0.2.9000
+Date: 2016-11-03
+Authors@R: c(
+  person("George", "Vega Yon", email="g.vegayon@gmail.com", role=c("aut", "cre")),
+  person("Enyelbert", "Muñoz", email="enyeldoc2011@gmail.com", role=c("ctb")))
+Description: An implementation of Karaboga (2005) Artificial Bee Colony
+    Optimization algorithm. This (working) version is a Work-in-progress, which is
+    why it has been implemented using pure R code. This was developed upon the basic
+    version programmed in C and distributed at the algorithm's official website.
 Classification/ACM: G.1.6
 Classification/JEL: C61
 Encoding: UTF-8
 URL: http://github.com/gvegayon/ABCoptim, http://mf.erciyes.edu.tr/abc/
 License: GPL (>= 3)
 LazyLoad: yes
+LinkingTo: Rcpp
+Imports:
+    Rcpp
+RoxygenNote: 5.0.1

NAMESPACE

@@ -1,2 +1,7 @@
-export(abc_optim)
+# Generated by roxygen2: do not edit by hand
 
+export(abc_cpp)
+export(abc_optim)
+importFrom(Rcpp,sourceCpp)
+importFrom(stats,runif)
+useDynLib(ABCoptim)

R/ABCoptim-package.R

@@ -0,0 +1,51 @@
+
+
+#' An implementation of the Artificial Bee Colony (ABC) Algorithm
+#' 
+#' This is an implementation of Karaboga (2005) ABC optimization algorithm. It
+#' was developed upon the basic version programmed in \code{C} and distributed
+#' at the algorithm's official website (see the references).
+#' 
+#' Please consider that this version is in alpha state of development, thus any
+#' evident (precision) error should be blaimed to the package author (not to
+#' the algorithm itself)
+#' 
+#' Please visit the project home for more information:
+#' \url{https://github.com/gvegayon/ABCoptim}.
+#' 
+#' \tabular{ll}{ Package: \tab ABCoptim\cr Type: \tab Package\cr Version: \tab
+#' 0.2.9000\cr Date: \tab 2016-11-03\cr License: \tab GPL version 2 or later\cr
+#' }
+#' 
+#' @name ABCoptim-package
+#' @aliases ABCoptim-package ABCoptim abc
+#' @docType package
+#' @author George Vega Yon \email{g.vegayon@@gmail.com} [aut],
+#' 
+#' Enyelbert Muñoz \email{enyeldoc2011@@gmail.com} [cnt)
+#' @references D. Karaboga, \emph{An Idea based on Honey Bee Swarm for
+#' Numerical Optimization}, tech. report TR06,Erciyes University, Engineering
+#' Faculty, Computer Engineering Department, 2005
+#' \url{http://mf.erciyes.edu.tr/abc/pub/tr06_2005.pdf}
+#' 
+#' Artificial Bee Colony (ABC) Algorithm (website)
+#' \url{http://mf.erciyes.edu.tr/abc/index.htm}
+#' 
+#' Basic version of the algorithm implemented in \code{C} (ABC's official
+#' website) \url{http://mf.erciyes.edu.tr/abc/form.aspx}
+#' @keywords package
+#' @examples
+#' 
+#'   \dontrun{
+#'     demo(ABCoptim) # Some functions...
+#'   }
+#' 
+NULL
+
+#' @useDynLib ABCoptim
+#' @importFrom Rcpp sourceCpp
+#' @importFrom stats runif
+NULL
+
+
+

R/RcppExports.R

@@ -0,0 +1,9 @@
+# Generated by using Rcpp::compileAttributes() -> do not edit by hand
+# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
+
+#' @rdname abc_optim
+#' @export
+abc_cpp <- function(par, fn, ub = 1e20, lb = -1e20, FoodNumber = 20L, limit = 100L, maxCycle = 1000L, criter = 50L) {
+    .Call('ABCoptim_abc_cpp', PACKAGE = 'ABCoptim', par, fn, ub, lb, FoodNumber, limit, maxCycle, criter)
+}
+

R/abc_optim.R

@@ -1,5 +1,70 @@
-rm(list=ls())
-
+#' Optimization through ABC algorithm
+#' 
+#' Optimizes through the ABC algorithm
+#' 
+#' This is an implementation of Karaboga (2005) ABC optimization algorithm. It
+#' was developed upon the basic version programmed in \code{C} and distributed
+#' at the algorithm's official website (see the references).
+#' 
+#' By default, lower and upper bounds are set as \code{+-Inf}. This last thing
+#' is just conceptual as all infinite bounds are replaced by
+#' \code{.Machine$double.xmax*1e-10} (still a pretty big number).
+#' 
+#' If \code{D} (the number of parameters to be optimzed) is greater than one,
+#' then \code{lb} and \code{ub} can be either scalars (assuming that all the
+#' parameters share the same boundaries) or vectors (the parameters have
+#' different boundaries each other).
+#' 
+#' @param par Initial values for the parameters to be optimized over
+#' @param fn A function to be minimized, with first argument of the vector of
+#' parameters over which minimization is to take place. It should return a
+#' scalar result.
+#' @param D Number of parameters to be optimized.
+#' @param ... Further arguments to be passed to 'fn'.
+#' @param NP Number of bees.
+#' @param FoodNumber Number of food sources to exploit.
+#' @param lb Lower bound of the parameters to be optimized.
+#' @param ub Upper bound of the parameters to be optimized.
+#' @param limit Limit of a food source.
+#' @param maxCycle Maximum number of iterations.
+#' @param optiinteger Whether to optimize binary parameters or not.
+#' @param criter Stop criteria (numer of unchanged results) until stopping
+#' @return A list containing the optimized parameters (\code{$par}), the value
+#' of the function (\code{$value}) and the number of iterations taken to reach
+#' the optimum (\code{$counts}).
+#' @author George Vega Yon \email{g.vegayon@@gmail.com}
+#' @references D. Karaboga, \emph{An Idea based on Honey Bee Swarm for
+#' Numerical Optimization}, tech. report TR06,Erciyes University, Engineering
+#' Faculty, Computer Engineering Department, 2005
+#' \url{http://mf.erciyes.edu.tr/abc/pub/tr06_2005.pdf}
+#' 
+#' Artificial Bee Colony (ABC) Algorithm (website)
+#' \url{http://mf.erciyes.edu.tr/abc/index.htm}
+#' 
+#' Basic version of the algorithm implemented in \code{C} (ABC's official
+#' website) \url{http://mf.erciyes.edu.tr/abc/form.aspx}
+#' @keywords optimization
+#' @examples
+#' 
+#' # EXAMPLE 1: The minimum is at (pi,pi)
+#' fun <- function(x) {
+#'   -cos(x[1])*cos(x[2])*exp(-((x[1] - pi)^2 + (x[2] - pi)^2))
+#' }
+#' 
+#' abc_optim(rep(0,2), fun, lb=-20, ub=20, criter=100)
+#' 
+#' # EXAMPLE 2: global minimum at about (-15.81515)
+#' fw <- function (x)
+#'   10*sin(0.3*x)*sin(1.3*x^2) + 0.00001*x^4 + 0.2*x+80
+#' 
+#' abc_optim(50, fw, lb=-100, ub=100, criter=100)
+#' 
+#' # EXAMPLE 3: 5D sphere, global minimum at about (0,0,0,0,0)
+#' fs <- function(x) sum(x^2)
+#' 
+#' abc_optim(rep(10,5), fs, lb=-100, ub=100, criter=200)
+#' 
+#' @export abc_optim
 abc_optim <- function(
   par,               # Vector de parametros a opti 
   fn,                # Funcion objetivo
@@ -111,27 +176,24 @@ abc_optim <- function(
 
   SendEmployedBees <- function() {
     for (i in 1:FoodNumber) {
-      r <- runif(1)
       # The parameter to be changed is determined randomly
-      param2change <- floor(r*D) + 1 
+      param2change <- sample(1:D, 1) # floor(runif(1)*D) + 1 
 
       # A randomly chosen solution is used in producing a mutant solution of the solution i
-      neighbour <- floor(r*FoodNumber) + 1 
-
       # Randomly selected solution must be different from the solution i
+      neighbour <- i
       while(neighbour==i)
-        neighbour <- floor(runif(1)*FoodNumber) + 1
+        neighbour <- sample(1:FoodNumber, 1) # floor(runif(1)*FoodNumber) + 1
 
       solution <<- Foods[i,]
 
       # v_{ij}=x_{ij}+\phi_{ij}*(x_{kj}-x_{ij}) 
-      r <- runif(1)
-
-      if (optiinteger) solution[param2change] <<- r > 0.5
+
+      if (optiinteger) solution[param2change] <<- runif(1) > 0.5
       else {
         solution[param2change] <<- 
           Foods[i,param2change]+
-          (Foods[i,param2change]-Foods[neighbour,param2change])*(r-0.5)*2
+          (Foods[i,param2change]-Foods[neighbour,param2change])*(runif(1)-0.5)*2
 
         # if generated parameter value is out of boundaries, it is shifted onto the boundaries
         if (solution[param2change]<lb[param2change])
@@ -182,33 +244,30 @@ abc_optim <- function(
     t <- 0
     while (t < FoodNumber)
     {
-      r <- runif(1)
+
       # choose a food source depending on its probability to be chosen
-      if (r < prob[i]) {
+      if (runif(1) < prob[i]) {
         t <- t + 1
-        r <- runif(1)
-
+
         # The parameter to be changed is determined randomly
-        param2change <- floor(r*D) + 1
+        param2change <- sample(1:D, 1) # floor(runif(1)*D) + 1 
 
         # A randomly chosen solution is used in producing a mutant solution of the solution i
-        neighbour <- floor(r*FoodNumber) + 1
-
         #Randomly selected solution must be different from the solution i*/        
+        neighbour <- i
         while(neighbour==i)
-          neighbour <- floor(runif(1)*FoodNumber) + 1
+          neighbour <- sample(1:FoodNumber, 1) # floor(runif(1)*FoodNumber) + 1
 
         solution <<- Foods[i,]
 
         # v_{ij}=x_{ij}+\phi_{ij}*(x_{kj}-x_{ij}) */
-        r <- runif(1)
-
-        if (optiinteger) solution[param2change] <<- r > .5
+
+        if (optiinteger) solution[param2change] <<- runif(1) > .5
         else 
         {
           solution[param2change] <<- 
             Foods[i,param2change]+
-            (Foods[i,param2change]-Foods[neighbour,param2change])*(r-0.5)*2
+            (Foods[i,param2change]-Foods[neighbour,param2change])*(runif(1)-0.5)*2
 
           # if generated parameter value is out of boundaries, it is shifted onto the boundaries*/
           if (solution[param2change]<lb[param2change]) 
@@ -282,59 +341,59 @@ abc_optim <- function(
 
 }
 
-################################################################################
-# Ejemplos
-################################################################################
-
-X <- c(3,2,3,1)
-
-# Funcion de matching
-fun <- function(lambda, x0, X, M)
-{
-  norm((x0 - X)*lambda, type="2") + exp(abs(sum(lambda > 0) - M))
-}
-
-# Mejor vecino para
-#  x0 = 2
-#  X  = c(3,2,3,1)
-#  M  = 1
-# El mejor resultado debe ser [0,1,0,0]
-x1 <- abc_optim(rep(0,4), fun, x0=2, X=X, M=1, lb=0, ub=1, optiinteger=T)
-x1
-
-# Mejores dos vecinos para
-#  x0 = 3
-#  X  = c(3,2,3,1)
-#  M  = 2
-# El mejor resultado debe ser [1,0,1,0]
-x2 <- abc_optim(rep(0,4), fun, x0=3, X=X, M=2, lb=0, ub=1, optiinteger=T)
-x2
-
-################################################################################
-# Definicion de la funcion
-fun <- function(x) {
-  -cos(x[1])*cos(x[2])*exp(-((x[1] - pi)^2 + (x[2] - pi)^2))
-}
-
-abc_optim(rep(0,2), fun, lb=-5, ub=5, criter=50)
-
-optim(rep(0,2), fn=fun) #lower=-5,upper=5)
-
-################################################################################
-# Definicion de la funcion
-
-fun <- function(x) {
-  -4+(x[1]^2 + x[2]^2)
-}
-
-abc_optim(c(1,1), fn=fun, lb=-100000, ub=100000,criter=100)
-
-################################################################################
-# Definicion de la funcion
-
-fun <- function(x) {
-  -(x^4 - 2*x^2 - 8)
-}
-
-abc_optim(0, fn=fun, lb=-2, ub=2,criter=100)
+# ################################################################################
+# # Ejemplos
+# ################################################################################
+# 
+# X <- c(3,2,3,1)
+# 
+# # Funcion de matching
+# fun <- function(lambda, x0, X, M)
+# {
+#   norm((x0 - X)*lambda, type="2") + exp(abs(sum(lambda > 0) - M))
+# }
+# 
+# # Mejor vecino para
+# #  x0 = 2
+# #  X  = c(3,2,3,1)
+# #  M  = 1
+# # El mejor resultado debe ser [0,1,0,0]
+# x1 <- abc_optim(rep(0,4), fun, x0=2, X=X, M=1, lb=0, ub=1, optiinteger=T)
+# x1
+# 
+# # Mejores dos vecinos para
+# #  x0 = 3
+# #  X  = c(3,2,3,1)
+# #  M  = 2
+# # El mejor resultado debe ser [1,0,1,0]
+# x2 <- abc_optim(rep(0,4), fun, x0=3, X=X, M=2, lb=0, ub=1, optiinteger=T)
+# x2
+# 
+# ################################################################################
+# # Definicion de la funcion
+# fun <- function(x) {
+#   -cos(x[1])*cos(x[2])*exp(-((x[1] - pi)^2 + (x[2] - pi)^2))
+# }
+# 
+# abc_optim(rep(0,2), fun, lb=-5, ub=5, criter=50)
+# 
+# optim(rep(0,2), fn=fun) #lower=-5,upper=5)
+# 
+# ################################################################################
+# # Definicion de la funcion
+# 
+# fun <- function(x) {
+#   -4+(x[1]^2 + x[2]^2)
+# }
+# 
+# abc_optim(c(1,1), fn=fun, lb=-100000, ub=100000,criter=100)
+# 
+# ################################################################################
+# # Definicion de la funcion
+# 
+# fun <- function(x) {
+#   -(x^4 - 2*x^2 - 8)
+# }
 # 
+# abc_optim(0, fn=fun, lb=-2, ub=2,criter=100)
+# #