gRaviopt-package: gRaviopt

In gRaviopt: What the package does (short line)

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: <[e-mail]>

References

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

Examples

## 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)

gRaviopt documentation built on May 2, 2019, 6:53 p.m.