High-Dimensional Multi-Objective Bayesian Optimization With Block Coordinate Updates: Case Studies in Intelligent Transportation System

Many transportation system problems can be formulated as high-dimensional expensive multi-objective problems. They are challenging for Gaussian process-based Bayesian optimization methods to find the Pareto fronts due to the curse of dimensionality and the boundary issue in the acquisition function...

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Veröffentlicht in:	IEEE transactions on intelligent transportation systems Jg. 25; H. 1; S. 1 - 12
Hauptverfasser:	Wang, Hongyan, Xu, Hua, Zhang, Zeqiu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.01.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	<inline-formula xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"> <tex-math notation="LaTeX"> theta</tex-math> </inline-formula>-dominance rank Additives Bayes methods Bayesian analysis Complexity Computational modeling Convergence convergence-diversity trade-offs Dimensionality reduction Gaussian process Gaussian processes High-dimensional expensive multi-objective problems Intelligent transportation systems Iterative methods multi-objective Bayesian optimization Multiple objective analysis Optimization Tradeoffs Transportation
ISSN:	1524-9050, 1558-0016
Online-Zugang:	Volltext
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