smco-package: A simple Monte Carlo optimizer using adaptive coordinate...

Description Details Author(s) References Examples

Description

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.

Details

Package: smco
Type: Package
Version: 1.0
Date: 2011-06-05
License: GPL (>= 2)
LazyLoad: yes

Unique function:

smco(): Simple Monte Carlo optimizer

Author(s)

Prof. Juan D. Velasquez, Ph.D.
Grupo de Computacion Aplicada
Univesidad Nacional de Colombia
[email protected]

References

Velasquez, J. D. (2011). A Simple Monte Carlo optimizer based on Adaptive Coordinate Sampling. Submitted to Operation Research Letters.

Examples

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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))

smco documentation built on May 29, 2017, 9:27 p.m.