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Title: SOP: parallel surrogate global optimization with Pareto center selection for computationally expensive single objective problems

Abstract

This paper presents a parallel surrogate-based global optimization method for computationally expensive objective functions that is more effective for larger numbers of processors. To reach this goal, we integrated concepts from multi-objective optimization and tabu search into, single objective, surrogate optimization. Our proposed derivative-free algorithm, called SOP, uses non-dominated sorting of points for which the expensive function has been previously evaluated. The two objectives are the expensive function value of the point and the minimum distance of the point to previously evaluated points. Based on the results of non-dominated sorting, P points from the sorted fronts are selected as centers from which many candidate points are generated by random perturbations. Based on surrogate approximation, the best candidate point is subsequently selected for expensive evaluation for each of the P centers, with simultaneous computation on P processors. Centers that previously did not generate good solutions are tabu with a given tenure. We show almost sure convergence of this algorithm under some conditions. The performance of SOP is compared with two RBF based methods. The test results show that SOP is an efficient method that can reduce time required to find a good near optimal solution. In a number of cases themore » efficiency of SOP is so good that SOP with 8 processors found an accurate answer in less wall-clock time than the other algorithms did with 32 processors.« less

Authors:
; ;
Publication Date:
Research Org.:
Cornell Univ., Ithaca, NY (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1328968
Alternate Identifier(s):
OSTI ID: 1425456
Grant/Contract Number:  
SC000 6791; SC0006791
Resource Type:
Published Article
Journal Name:
Journal of Global Optimization
Additional Journal Information:
Journal Name: Journal of Global Optimization Journal Volume: 66 Journal Issue: 3; Journal ID: ISSN 0925-5001
Publisher:
Springer Science + Business Media
Country of Publication:
Netherlands
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Krityakierne, Tipaluck, Akhtar, Taimoor, and Shoemaker, Christine A. SOP: parallel surrogate global optimization with Pareto center selection for computationally expensive single objective problems. Netherlands: N. p., 2016. Web. doi:10.1007/s10898-016-0407-7.
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Krityakierne, Tipaluck, Akhtar, Taimoor, & Shoemaker, Christine A. SOP: parallel surrogate global optimization with Pareto center selection for computationally expensive single objective problems. Netherlands. https://doi.org/10.1007/s10898-016-0407-7
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Krityakierne, Tipaluck, Akhtar, Taimoor, and Shoemaker, Christine A. Tue . "SOP: parallel surrogate global optimization with Pareto center selection for computationally expensive single objective problems". Netherlands. https://doi.org/10.1007/s10898-016-0407-7.
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@article{osti_1328968,
title = {SOP: parallel surrogate global optimization with Pareto center selection for computationally expensive single objective problems},
author = {Krityakierne, Tipaluck and Akhtar, Taimoor and Shoemaker, Christine A.},
abstractNote = {This paper presents a parallel surrogate-based global optimization method for computationally expensive objective functions that is more effective for larger numbers of processors. To reach this goal, we integrated concepts from multi-objective optimization and tabu search into, single objective, surrogate optimization. Our proposed derivative-free algorithm, called SOP, uses non-dominated sorting of points for which the expensive function has been previously evaluated. The two objectives are the expensive function value of the point and the minimum distance of the point to previously evaluated points. Based on the results of non-dominated sorting, P points from the sorted fronts are selected as centers from which many candidate points are generated by random perturbations. Based on surrogate approximation, the best candidate point is subsequently selected for expensive evaluation for each of the P centers, with simultaneous computation on P processors. Centers that previously did not generate good solutions are tabu with a given tenure. We show almost sure convergence of this algorithm under some conditions. The performance of SOP is compared with two RBF based methods. The test results show that SOP is an efficient method that can reduce time required to find a good near optimal solution. In a number of cases the efficiency of SOP is so good that SOP with 8 processors found an accurate answer in less wall-clock time than the other algorithms did with 32 processors.},
doi = {10.1007/s10898-016-0407-7},
journal = {Journal of Global Optimization},
number = 3,
volume = 66,
place = {Netherlands},
year = {Tue Feb 02 00:00:00 EST 2016},
month = {Tue Feb 02 00:00:00 EST 2016}
}
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https://doi.org/10.1007/s10898-016-0407-7
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Cited by: 24 works
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Works referenced in this record:

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    Works referencing / citing this record:

    Multi-surrogate-based global optimization using a score-based infill criterion
    journal, September 2018

    • Dong, Huachao; Sun, Siqing; Song, Baowei
    • Structural and Multidisciplinary Optimization, Vol. 59, Issue 2
    • DOI: 10.1007/s00158-018-2079-z

    On the choice of the low-dimensional domain for global optimization via random embeddings
    journal, October 2019

    • Binois, Mickaël; Ginsbourger, David; Roustant, Olivier
    • Journal of Global Optimization, Vol. 76, Issue 1
    • DOI: 10.1007/s10898-019-00839-1
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