scheduleSPSANN: 'spsann' annealing schedule

In spsann: Optimization of Sample Configurations using Spatial Simulated Annealing

Description

Set the control parameters for the annealing schedule of spsann functions.

Usage

scheduleSPSANN(initial.acceptance = 0.95, initial.temperature = 0.001,
  temperature.decrease = 0.95, chains = 500, chain.length = 1,
  stopping = 10, x.max, x.min = 0, y.max, y.min = 0, cellsize)

Arguments

initial.acceptance	Numeric value between 0 and 1 defining the initial acceptance probability, i.e. the proportion of proposed system configurations that should be accepted in the first chain. The optimization is stopped and a warning is issued if this value is not attained. Defaults to initial.acceptance = 0.95.
initial.temperature	Numeric value larger than 0 defining the initial temperature of the system. A low initial.temperature, combined with a low initial.acceptance result in the algorithm to behave as a greedy algorithm, i.e. only better system configurations are accepted. Defaults to initial.temperature = 0.001.
temperature.decrease	Numeric value between 0 and 1 used as a multiplying factor to decrease the temperature at the end of each Markov chain. Defaults to temperature.decrease = 0.95.
chains	Integer value defining the maximum number of chains, i.e. the number of cycles of jitters at which the temperature and the size of the neighbourhood should be kept constant. Defaults to chains = 500.
chain.length	Integer value defining the length of each Markov chain relative to the number of sample points. Defaults to chain.length = 1, i.e. one time the number of sample points.
stopping	Integer value defining the maximum allowable number of Markov chains without improvement of the objective function value. Defaults to stopping = 10.
x.max, x.min, y.max, y.min	Numeric value defining the minimum and maximum quantity of random noise to be added to the projected x- and y-coordinates. The units are the same as of the projected x- and y-coordinates. If missing, they are estimated from candi, x.min and y.min being set to zero, and x.max and y.max being set to half the maximum distance in the x- and y-coordinates, respectively.
cellsize	Vector with two numeric values defining the horizontal (x) and vertical (y) spacing between the candidate locations in candi. A single value can be used if the spacing in the x- and y-coordinates is the same. If cellsize = 0 then spsann understands that a finite set of candidate locations is being used (See Details).

Value

A list with a set of control parameters of the annealing schedule.

Author(s)

Alessandro Samuel-Rosa [e-mail]

References

Aarts, E. H. L.; Korst, J. H. M. Boltzmann machines for travelling salesman problems. European Journal of Operational Research, v. 39, p. 79-95, 1989.

Černý, V. Thermodynamical approach to the travelling salesman problem: an efficient simulation algorithm. Journal of Optimization Theory and Applications, v. 45, p. 41-51, 1985.

Brus, D. J.; Heuvelink, G. B. M. Optimization of sample patterns for universal kriging of environmental variables. Geoderma, v. 138, p. 86-95, 2007.

Kirkpatrick, S.; Gelatt, C. D.; Vecchi, M. P. Optimization by simulated annealing. Science, v. 220, p. 671-680, 1983.

Metropolis, N.; Rosenbluth, A. W.; Rosenbluth, M. N.; Teller, A. H.; Teller, E. Equation of state calculations by fast computing machines. The Journal of Chemical Physics, v. 21, p. 1087-1092, 1953.

van Groenigen, J.-W.; Stein, A. Constrained optimization of spatial sampling using continuous simulated annealing. Journal of Environmental Quality. v. 27, p. 1078-1086, 1998.

Webster, R.; Lark, R. M. Field sampling for environmental science and management. London: Routledge, p. 200, 2013.

See Also

optimACDC, optimCORR, optimDIST, optimMKV, optimMSSD, optimPPL, optimSPAN, optimUSER.

Examples

schedule <- scheduleSPSANN()

Example output

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Optimization of Sample Configurations using Spatial Simulated
Annealing 
spsann version 2.2.0 
(built on 2019-04-28) is now loaded                
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spsann documentation built on May 2, 2019, 1:36 p.m.