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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