Data-driven spatial branch-and-bound algorithms for box-constrained simulation-based optimization

The ability to use complex computer simulations in quantitative analysis and decision-making is highly desired in science and engineering, at the same rate as computation capabilities and first-principle knowledge advance. Due to the complexity of simulation models, direct embedding of equation-base...

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Bibliographic Details
Published in:Journal of global optimization Vol. 82; no. 1; pp. 21 - 50
Main Authors: Zhai, Jianyuan, Boukouvala, Fani
Format: Journal Article
Language:English
Published: New York Springer US 01.01.2022
Springer
Springer Nature B.V
Subjects:
Algorithms
Complexity
Computer Science
Computer simulation
Computer-generated environments
Decision analysis
Decision making
First principles
Mathematical optimization
Mathematics
Mathematics and Statistics
Operations Research/Decision Theory
Optimization
Optimization techniques
Real Functions
Simulation
Spatial data
Branch-and-bound
Simulation-optimization
Convex underestimators
Global optimization
Black-box optimization
ISSN:0925-5001, 1573-2916
Online Access:Get full text
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Summary:The ability to use complex computer simulations in quantitative analysis and decision-making is highly desired in science and engineering, at the same rate as computation capabilities and first-principle knowledge advance. Due to the complexity of simulation models, direct embedding of equation-based optimization solvers may be impractical and data-driven optimization techniques are often needed. In this work, we present a novel data-driven spatial branch-and-bound algorithm for simulation-based optimization problems with box constraints, aiming for consistent globally convergent solutions. The main contribution of this paper is the introduction of the concept data-driven convex underestimators of data and surrogate functions, which are employed within a spatial branch-and-bound algorithm. The algorithm is showcased by an illustrative example and is then extensively studied via computational experiments on a large set of benchmark problems.
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ISSN:0925-5001
1573-2916
DOI:10.1007/s10898-021-01045-8