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high-dimensional multi-objective Bayesian Optimization With Block Coordinate Updates: Case Studies in Intelligent Transportation System

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第1期25卷 884-895页

作者： Wang, Hongyan Xu, Hua Zhang, Zeqiu Tsinghua Univ Dept Comp Sci & Technol State Key Lab Intelligent Technol & Syst Beijing 100084 Peoples R China Huawei Technol Beijing 100095 Peoples R China

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 optimization. This paper presents a multi-objective Bayesian optimization method with block coordinate updates, Block-MOBO, to solve high-dimensional expensive multi-objective problems. Block-MOBO first partitions the decision variable space into different blocks, each of which includes a low-dimensional multi-objective problem. At each iteration, one block is considered and the decision variables not in this block are approximated by context-vector generation embedded with the Pareto prior knowl-edge thus promoting convergence. To tackle the boundary issue, we present ?-greedy acquisition function in a Bayesian and multi-objective fashion, which recommends candidates either from the exploitation-exploration trade-off perspective or with probability ? from the Pareto dominance relationship perspective. We verify the effectiveness of Block-MOBO by comparing it with other multi-objective Bayesian methods on two real-world optimization problems in transportation system and three multi-objective synthetic test suites. The experimental results show that Block-MOBO can find more evenly distributed and non-dominated solutions in the whole search space with lower complexity compared with other state-of-the-art approaches. Our analyses illustrate that block coordinate updates and ?-greedy acquisition function contribute to computational complexity reduction and convergence-diversity trade-offs, respectively.

关键词： high-dimensional expensive multi-objective problems multi-objective Bayesian optimization Gaussian process ?-dominance rank convergence-diversity trade-offs

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