Abstract
Stochastic optimization procedures have been shown to be efficient methods for finding global extrema of objective functions. In this article we report computational results obtained using the generalized simulated annealing method on a set of standard global optimization test problems. The results are compared to those obtained using the best known competing stochastic optimization methods.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 425-449 |
| Number of pages | 25 |
| Journal | American Journal of Mathematical and Management Sciences |
| Volume | 8 |
| Issue number | 3-4 |
| DOIs | |
| State | Published - 1988 |
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Keywords
- Annealing
- Global optimization
- Stochastic optimization
ASJC Scopus subject areas
- Business, Management and Accounting(all)
- Applied Mathematics
Cite this
Computational experience with generalized simulated annealing over continuous variables. / Brooks, Daniel G.; Verdini, William A.
In: American Journal of Mathematical and Management Sciences, Vol. 8, No. 3-4, 1988, p. 425-449.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Computational experience with generalized simulated annealing over continuous variables
AU - Brooks, Daniel G.
AU - Verdini, William A.
PY - 1988
Y1 - 1988
N2 - Stochastic optimization procedures have been shown to be efficient methods for finding global extrema of objective functions. In this article we report computational results obtained using the generalized simulated annealing method on a set of standard global optimization test problems. The results are compared to those obtained using the best known competing stochastic optimization methods.
AB - Stochastic optimization procedures have been shown to be efficient methods for finding global extrema of objective functions. In this article we report computational results obtained using the generalized simulated annealing method on a set of standard global optimization test problems. The results are compared to those obtained using the best known competing stochastic optimization methods.
KW - Annealing
KW - Global optimization
KW - Stochastic optimization
UR - http://www.scopus.com/inward/record.url?scp=0001072959&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0001072959&partnerID=8YFLogxK
U2 - 10.1080/01966324.1988.10737248
DO - 10.1080/01966324.1988.10737248
M3 - Article
AN - SCOPUS:0001072959
VL - 8
SP - 425
EP - 449
JO - American Journal of Mathematical and Management Sciences
JF - American Journal of Mathematical and Management Sciences
SN - 0196-6324
IS - 3-4
ER -