gRaviopt-package: gRaviopt

Description

Implementation of an stochastic agent-based optimisation algorithm using the laws of gravity and motion. Loosely based on the CSS algorithm of A. Kaveh and S. Talatahari described in "A novel heuristic optimization method: charged system search", Acta Mech 213, 267-289 (2010).

Details

Package: gRaviopt
Type: Package
Version: 1.0
Date: 2011-05-06
License: GNU
LazyLoad: yes

Author(s)

Peter Kehler Maintainer: <peter.kehler.jr@googlemail.com>

References

A. Kaveh and S. Talatahari: A novel heuristic optimization method: charged system search, Acta Mech 213, 267–289 (2010)

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
## Rastrigin02 function
## The function has a global maximum f(x) = 0 at the point (0,0).  
## gRaviopt searches for maxima of the objective function between
## lower and upper bounds on each parameter to be optimized. 

Rastrigin02  <- function(X){
  -((X[,1]^2 - 10*cos(2*pi*X[,1]^2) + 10) + (X[,2]^2 - 10*cos(2*pi*X[,2]^2) + 10))
}

## This version of the function is needed for gRaviopt.Plot
Rastrigin02.2d  <- function(x,y){
  -((x*x - 10*cos(2*pi*x) + 10) + (y*y - 10*cos(2*pi*y) + 10))
}

# optimization process of Rastrigin02
Rast02 <- gRaviopt(fn= Rastrigin02, Par=2, n=20, lower.limits = -3, upper.limits = 3,man.scaling=TRUE,alpha=0.05)

# the best solutions found
Rast02$Memory

# the movements of the particles during the optimization process
gRaviopt.Plot(fn= Rastrigin02.2d, gRaviopt.Result=Rast02, Par=2, iterations=200, n=20, lower.limits = -3, upper.limits = 3, Movements=TRUE,man.scaling=TRUE,alpha=0.1,Nice=FALSE)

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

All documentation is copyright its authors; we didn't write any of that.