# 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: <[email protected]>

## 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) ```

gRaviopt documentation built on May 31, 2017, 5:15 a.m.