×

zbMATH — the first resource for mathematics

A Monte Carlo simulated annealing approach to optimization over continuous variables. (English) Zbl 0551.65045
Numerical optimization methods based on thermodynamic concepts are extended to the case of continuous multidimensional parameter spaces. Techniques which allow this strategy to be implemented efficiently and reliably, including a self-regulatory mechanism for choosing the random step distribution, are described. The method is applied to a set of standard global minimization problems, and to a typical non-linear least- squares functional fitting problem.

MSC:
65K05 Numerical mathematical programming methods
65D10 Numerical smoothing, curve fitting
65C05 Monte Carlo methods
PDF BibTeX XML Cite
Full Text: DOI
References:
[1] Kirkpatrick, S.; Gelatt, C.D.; Vecchi, M.P., Optimization by simulated annealing, Science, 220, 671, (1983) · Zbl 1225.90162
[2] Metropolis, N.; Rosenbluth, A.; Rosenbluth, M.; Teller, A.; Teller, E., J. chem. phys., 21, 1087, (1953)
[3] Dahlquist, G.; Bjoerck, A., Numerical methods, (1974), Prentice-Hall Englewood Cliffs, N.J, See, for example
[4] Dixon, L.C.W.; Szego, G.P., (), 1-18
[5] Newer, J.A.; Mead, R., Comput. J., 7, 308, (1967)
[6] Torn, A.A., (), 49-62
[7] Gomulka, J., (), 63-70
[8] Price, W.L., (), 71-84
[9] Mockus, J.; Tiesis, V.; Zilinskas, A., (), 117-130
[10] De Biase, L.; Frontini, F., (), 85-102
[11] Hamann, D.R.; Schluter, M.; Chiang, C., Phys. rev. lett., 43, 1494, (1979)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.