Annealing evolutionary stochastic approximation Monte Carlo for global optimization

Handle URI:
http://hdl.handle.net/10754/597576
Title:
Annealing evolutionary stochastic approximation Monte Carlo for global optimization
Authors:
Liang, Faming
Abstract:
In this paper, we propose a new algorithm, the so-called annealing evolutionary stochastic approximation Monte Carlo (AESAMC) algorithm as a general optimization technique, and study its convergence. AESAMC possesses a self-adjusting mechanism, whose target distribution can be adapted at each iteration according to the current samples. Thus, AESAMC falls into the class of adaptive Monte Carlo methods. This mechanism also makes AESAMC less trapped by local energy minima than nonadaptive MCMC algorithms. Under mild conditions, we show that AESAMC can converge weakly toward a neighboring set of global minima in the space of energy. AESAMC is tested on multiple optimization problems. The numerical results indicate that AESAMC can potentially outperform simulated annealing, the genetic algorithm, annealing stochastic approximation Monte Carlo, and some other metaheuristics in function optimization. © 2010 Springer Science+Business Media, LLC.
Citation:
Liang F (2010) Annealing evolutionary stochastic approximation Monte Carlo for global optimization. Stat Comput 21: 375–393. Available: http://dx.doi.org/10.1007/s11222-010-9176-1.
Publisher:
Springer Nature
Journal:
Statistics and Computing
KAUST Grant Number:
KUS-C1-016-04
Issue Date:
8-Apr-2010
DOI:
10.1007/s11222-010-9176-1
Type:
Article
ISSN:
0960-3174; 1573-1375
Sponsors:
The author's research was supported in part by the grant (DMS-0607755) made by the National Science Foundation and the award (KUS-C1-016-04) made by King Abdullah University of Science and Technology (KAUST). The author thanks the editor, the associate editor and the referees for their comments which have led to significant improvement of this paper.
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorLiang, Famingen
dc.date.accessioned2016-02-25T12:42:22Zen
dc.date.available2016-02-25T12:42:22Zen
dc.date.issued2010-04-08en
dc.identifier.citationLiang F (2010) Annealing evolutionary stochastic approximation Monte Carlo for global optimization. Stat Comput 21: 375–393. Available: http://dx.doi.org/10.1007/s11222-010-9176-1.en
dc.identifier.issn0960-3174en
dc.identifier.issn1573-1375en
dc.identifier.doi10.1007/s11222-010-9176-1en
dc.identifier.urihttp://hdl.handle.net/10754/597576en
dc.description.abstractIn this paper, we propose a new algorithm, the so-called annealing evolutionary stochastic approximation Monte Carlo (AESAMC) algorithm as a general optimization technique, and study its convergence. AESAMC possesses a self-adjusting mechanism, whose target distribution can be adapted at each iteration according to the current samples. Thus, AESAMC falls into the class of adaptive Monte Carlo methods. This mechanism also makes AESAMC less trapped by local energy minima than nonadaptive MCMC algorithms. Under mild conditions, we show that AESAMC can converge weakly toward a neighboring set of global minima in the space of energy. AESAMC is tested on multiple optimization problems. The numerical results indicate that AESAMC can potentially outperform simulated annealing, the genetic algorithm, annealing stochastic approximation Monte Carlo, and some other metaheuristics in function optimization. © 2010 Springer Science+Business Media, LLC.en
dc.description.sponsorshipThe author's research was supported in part by the grant (DMS-0607755) made by the National Science Foundation and the award (KUS-C1-016-04) made by King Abdullah University of Science and Technology (KAUST). The author thanks the editor, the associate editor and the referees for their comments which have led to significant improvement of this paper.en
dc.publisherSpringer Natureen
dc.subjectConvergenceen
dc.subjectGenetic algorithmen
dc.subjectGlobal optimizationen
dc.subjectSimulated annealingen
dc.subjectStochastic approximation Monte Carloen
dc.titleAnnealing evolutionary stochastic approximation Monte Carlo for global optimizationen
dc.typeArticleen
dc.identifier.journalStatistics and Computingen
dc.contributor.institutionTexas A and M University, College Station, United Statesen
kaust.grant.numberKUS-C1-016-04en
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.