gRaviopt.Plot: graphical output for functions with two parameters

In gRaviopt: What the package does (short line)

Description

visualisises the motion of the agents during the optimization process

Usage

gRaviopt.Plot(fn, gRaviopt.Result, Par = 2, iterations = 200, n = 20, lower.limits = -10, upper.limits = 10, Movements = FALSE, Nice = TRUE, man.scaling = F, alpha = 0.1)

Arguments

fn	needs a 2D version of the function that is optimized with gRaviopt
gRaviopt.Result	graphical output
Par	number of parameters used. Par should always be two.
iterations	number of iterations used for the optimization process
n	number of agents used for the optimization
lower.limits	the lower limits of the parameters used in "fn".
upper.limits	the upper limits of the parameters used in "fn".
Movements	should the movements be visualised or a static graphic of the optimized function
Nice	if FALSE no velocity, force vectors or new positions are drawn
man.scaling	the user can specicify wether to choose the the radius of local optimization manually or automatically.
alpha	radius that divises between local and global optimization.

Value

either graphical output of the optimized function (usind persp and image) or the movements of the agents during the optimization process

Author(s)

Peter Kehler [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.