Solving higher dimensional expensive black box global optimization problems using sparse directional search on surrogates

We present a new algorithm Directional Optimization Search with Surrogate (DOSS), for optimizing problems with box constraints and a computationally expensive black-box objective function. DOSS is a radial basis function (RBF) based method that mainly focuses on higher dimensional and computationall...

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Veröffentlicht in:Mathematical programming computation Jg. 16; H. 4; S. 665 - 693
Hauptverfasser: Liu, Limeng, Yang, Muming, Shoemaker, Christine A., Xie, Tingting
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2024
Springer Nature B.V
Schlagworte:
Algorithms
Black boxes
Full Length Paper
Gaussian process
Global optimization
Mathematics
Mathematics and Statistics
Mathematics of Computing
Operations Research/Decision Theory
Optimization
Radial basis function
Theory of Computation
Trajectory optimization
Trajectory planning
90-08 Computational methods for problems pertaining to operations research and mathematical programming
90C26 Nonconvex programming, global optimization
ISSN:1867-2949, 1867-2957
Online-Zugang:Volltext
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Zusammenfassung:We present a new algorithm Directional Optimization Search with Surrogate (DOSS), for optimizing problems with box constraints and a computationally expensive black-box objective function. DOSS is a radial basis function (RBF) based method that mainly focuses on higher dimensional and computationally expensive objective functions that can be multimodal. DOSS introduces three new techniques not previously used in earlier RBF algorithms, including using a combination of the coordinates knowledge level, fewer initial sampling points, and the surrogate’s gradient information. Numerical results on a test set including 14 test problems with 36, 48, and 60 dimensions show that DOSS outperforms two recently published algorithms RBFOpt and TuRBO, and earlier RBF algorithms such as DYCORS. TuRBO is a Gaussian process based optimization algorithm, which outperformed earlier state-of-the-art methods. DOSS algorithm also has a good performance on real-world optimization problems, including robot pushing and rover trajectory planning problems. Almost sure convergence for the DOSS algorithm is also proven in this paper. An implementation of DOSS is available at https://doi.org/10.5281/zenodo.13731558 .
Bibliographie:ObjectType-Article-1
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ISSN:1867-2949
1867-2957
DOI:10.1007/s12532-024-00267-7