Description Details Author(s) References Examples
This package introduces a novel approach to optimize non-linear complex functions based on three simple ideas: first, the thus sampling of each component of the solution vector, one at a time, based on a truncated normal distribution; second, the evolution of the standard deviation of the sampling distribution in each iteration, as a mechanism of self-adaptation; and third, the restart of the algorithm for escaping of local optima.
Package: | smco |
Type: | Package |
Version: | 1.0 |
Date: | 2011-06-05 |
License: | GPL (>= 2) |
LazyLoad: | yes |
Unique function:
smco(): | Simple Monte Carlo optimizer |
Prof. Juan D. Velasquez, Ph.D.
Grupo de Computacion Aplicada
Univesidad Nacional de Colombia
jdvelasq@unal.edu.co
Velasquez, J. D. (2011). A Simple Monte Carlo optimizer based on Adaptive Coordinate Sampling. Submitted to Operation Research Letters.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | SphereModel.fcn <-
function( x ) {
return(sum(x ^ 2))
}
f = SphereModel.fcn;
ndim = 10;
LB = rep( -600.000, ndim);
UB = rep( 600.000, ndim);
maxiter = 100;
s = smco(par = NULL, fn = SphereModel.fcn, N = ndim, LB = LB,
UB = UB, maxiter = maxiter, Co = 0.01, Cmin = 0.0001,
Cmax = 0.5, trc = TRUE, lambda = 20,
useBFGS = TRUE, control = list(maxit = 10))
|
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