Adaptive search with stochastic acceptance probabilities for global optimization

Archis Ghate; Robert L. Smith

doi:10.1016/j.orl.2007.10.005

Adaptive search with stochastic acceptance probabilities for global optimization

Archis Ghate, Robert L. Smith

Research output: Contribution to journal › Article › peer-review

27 Scopus citations

Abstract

We present an extension of continuous domain Simulated Annealing. Our algorithm employs a globally reaching candidate generator, adaptive stochastic acceptance probabilities, and converges in probability to the optimal value. An application to simulation-optimization problems with asymptotically diminishing errors is presented. Numerical results on a noisy protein-folding problem are included.

Original language	English (US)
Pages (from-to)	285-290
Number of pages	6
Journal	Operations Research Letters
Volume	36
Issue number	3
DOIs	https://doi.org/10.1016/j.orl.2007.10.005
State	Published - May 2008
Externally published	Yes

Keywords

Markov chain Monte Carlo
Noisy objective functions
Simulated annealing

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10.1016/j.orl.2007.10.005

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title = "Adaptive search with stochastic acceptance probabilities for global optimization",

abstract = "We present an extension of continuous domain Simulated Annealing. Our algorithm employs a globally reaching candidate generator, adaptive stochastic acceptance probabilities, and converges in probability to the optimal value. An application to simulation-optimization problems with asymptotically diminishing errors is presented. Numerical results on a noisy protein-folding problem are included.",

keywords = "Markov chain Monte Carlo, Noisy objective functions, Simulated annealing",

author = "Archis Ghate and Smith, {Robert L.}",

year = "2008",

month = may,

doi = "10.1016/j.orl.2007.10.005",

language = "English (US)",

volume = "36",

pages = "285--290",

journal = "Operations Research Letters",

issn = "0167-6377",

publisher = "Elsevier B.V.",

number = "3",

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TY - JOUR

T1 - Adaptive search with stochastic acceptance probabilities for global optimization

AU - Ghate, Archis

AU - Smith, Robert L.

PY - 2008/5

Y1 - 2008/5

N2 - We present an extension of continuous domain Simulated Annealing. Our algorithm employs a globally reaching candidate generator, adaptive stochastic acceptance probabilities, and converges in probability to the optimal value. An application to simulation-optimization problems with asymptotically diminishing errors is presented. Numerical results on a noisy protein-folding problem are included.

AB - We present an extension of continuous domain Simulated Annealing. Our algorithm employs a globally reaching candidate generator, adaptive stochastic acceptance probabilities, and converges in probability to the optimal value. An application to simulation-optimization problems with asymptotically diminishing errors is presented. Numerical results on a noisy protein-folding problem are included.

KW - Markov chain Monte Carlo

KW - Noisy objective functions

KW - Simulated annealing

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UR - http://www.scopus.com/inward/citedby.url?scp=43049109164&partnerID=8YFLogxK

U2 - 10.1016/j.orl.2007.10.005

DO - 10.1016/j.orl.2007.10.005

M3 - Article

AN - SCOPUS:43049109164

SN - 0167-6377

VL - 36

SP - 285

EP - 290

JO - Operations Research Letters

JF - Operations Research Letters

IS - 3

ER -

Adaptive search with stochastic acceptance probabilities for global optimization

Abstract

Keywords

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