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Parallel multi-objective bayesian optimization approaches based on multi-fidelity surrogate modeling

AEROSPACE SCIENCE AND TECHNOLOGY 2023年 143卷

作者： Lin, Quan Hu, Jiexiang Zhou, Qi Huazhong Univ Sci & Technol Sch Aerosp Engn Wuhan 430074 Peoples R China

Aerospace product design optimizations, such as micro-aerial vehicle fuselage design, often involve multiple objectives. multi-objective bayesian optimization (MOBO) is an efficient approach in solving problems concerning multiple conflicting objectives. Conventional MOBO approaches are often limited by sequential optimizations, which can only add one sample in each iteration. The parallel computing strategy is a desirable way to accelerate the optimization process by adding a batch of samples in one iteration. Unfortunately, existing parallel MOBO approaches are constrained to handling single-fidelity data, thereby missing out on the potential benefits of utilizing auxiliary low-fidelity data. To further improve the optimization efficiency of the parallel MOBO approach, this paper proposes two parallel MOBO approaches based on multi-fidelity surrogate modeling. Specially, the cheap auxiliary low-fidelity data can be utilized to improve the performance of the parallel MOBO approach by multi-fidelity surrogate modeling. The updating points and fidelity levels are determined by a modified hypervolume excepted improvement function, and two parallel computing strategies are developed for multi-point sampling. Additionally, a constraint handling strategy is developed for problems with constraints by adopting the probability of feasibility functions. The proposed approaches are demonstrated through numerical benchmark examples and two real-world applications involving the multi-objective optimizations of a microaerial vehicle fuselage and a metamaterial vibration isolator. Results in the real-world applications show that the proposed approaches significantly improve the optimization efficiency with faster convergence speed and exhibit superior overall performance compared with the state-of-the-art MOBO methods.

关键词： multi-objective bayesian optimization multi-fidelity surrogate Parallel computing Micro-aerial vehicle fuselage

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Non-Uniform Metamaterial Mushroom Antennas via a Genuine multi-objective bayesian optimization Method 18

Non-Uniform Metamaterial Mushroom Antennas via a Genuine Mul...

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18th European Conference on Antennas and Propagation (EuCAP)

作者： Zeng, Yunjia Qing, Xianming Chia, Michael Yan-Wah ASTAR Inst Infocomm Res Singapore Singapore

ISBN: (纸本)9788831299091;9798350394436

Three broadband non-uniform metamaterial mushroom antennas are designed with distinct mushroom configurations. The design trade-offs among the input impedance bandwidth, boresight gain, and the front-to-back ratio are considered with a genuine multi-objective bayesian optimization method. The three non-uniform antennas attain a notable increase in impedance bandwidth with different degrees of trade-off in terms of the other two design targets when compared to the reference antenna with uniform mushroom configuration. All the four antennas are prototyped and measured, with strong alignment between simulation and measurement. The optimized designs can function in the n77 band of 5G New Radio.

关键词： Mushroom Antenna Microstrip Antenna multi-objective bayesian optimization Design Trade-off 5G New Radio

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A multi-objective bayesian optimization Algorithm with Online Knowledge Transfer 6

A Multi-objective Bayesian Optimization Algorithm with Onlin...

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6th International Conference on Data-Driven optimization of Complex Systems (DOCS)

作者： Li, Huiting Jin, Yaochu Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang Peoples R China Westlake Univ Sch Engn Hangzhou Peoples R China

ISBN: (纸本)9798350377859;9798350377842

Since transfer learning can learn effective knowledge from the source task to help solve the target task, it offers a powerful tool for assisting multi-objective evolutionary bayesian optimization in solving expensive multi-objective problems in the presence of data paucity using. Most existing optimization algorithms use genetic operators or construct Gaussian process models to achieve knowledge transfer and much less work have been reported that make use of transfer learning methods for knowledge transfer. To fill the gap, this paper proposes an online knowledge transfer-based multi-objective bayesian optimization algorithm. The algorithm first classifies the data of cheap and expensive problems based on the dominance relationship and trains a classifier between cheap and expensive problems using the balanced distribution adaptation method. Then, this classifier is employed to generate synthetic data for training the surrogates for expensive optimization. In addition, to enhance the prediction accuracy for the classifier, an online transfer approach is adopted to retrain the classifier using the newly evaluated data in each iteration of optimization. Finally, the proposed algorithm is examined in comparison with two popular algorithms on two benchmark test problems to verify its performance.

关键词： Knowledge transfer multi-objective bayesian optimization expensive problems classifier

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A New Acquisition Function for multi-objective bayesian optimization: Correlated Probability of Improvement

A New Acquisition Function for Multi-objective Bayesian Opti...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Yang, Kaifeng Chen, Kai Affenzeller, Michael Werth, Bernhard Univ Appl Sci Upper Austria Heurist & Evolutionary Algorithms Lab Hagenberg Austria Cent South Univ Sch Math & Stat Changsha Peoples R China

ISBN: (纸本)9798400701207

multi-objective bayesian optimization is a sequential optimization strategy in which an optimizer searches for optimal solutions by maximizing an acquisition function. Most existing acquisition functions assume that objectives are independent, but none of them incorporates the correlations among objectives through an explicit formula for exact computation. This paper proposes a novel acquisition function, namely, correlated probability of improvement (cPoI), for bi-objective optimization problems. The cPoI method builds on the probability of improvement and addresses the correlations between objectives by utilizing 3 distinct approaches to compute the posterior covariance matrix from a multi-task Gaussian process. This paper presents both an explicit formula for exact computation of cPoI and a Monte Carlo method for approximating it. We evaluate the performance of the proposed cPoI against 4 state-of-the-art multi-objective optimization algorithms on 8 artificial benchmarks and 1 real-world problem. Our experimental results demonstrate the effectiveness of cPoI in achieving superior optimization performance.

关键词： multi-objective bayesian optimization multi-task Gaussian Process Posterior Covariance Probability of Improvement

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An efficient parallel multi-fidelity multi-objective bayesian optimization method and application to 3-stage axial compressor with 144 variables

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AEROSPACE SCIENCE AND TECHNOLOGY 2024年 150卷

作者： He, Youwei Gui, Qingwen Luo, Jinliang Univ South China Sch Mech Engn Hengyang 421001 Peoples R China

Parallel infill sampling is a promising approach to improve the efficiency of multi-fidelity multi-objective bayesian optimization (MOBO). In existing literature, the number of infill samples per iteration is typically limited to 10. Additionally, the application of the multi-fidelity MOBO method in engineering optimization designs with over 100 variables is rare. To that end, a novel generalized expected improvement matrix (GEIM) criterion is proposed by using generalized reference values for the element in expected improvement matrix. Parallel infill sampling strategy based on GEIM is developed and incorporated into the multi-fidelity MOBO framework. The distinct feature of the proposed method is that the number of infill samples at each iteration can be significantly larger than existing methods (i.e. as much as 100), so as to further enhance the optimization efficiency. Empirical experiment results on analytic problems show that the proposed parallel multi-fidelity MOBO method is highly competitive in comparison with existing methods. To address the curse of dimensionality in optimizing multi-stage axial compressor, an efficient modeling method for the Hierarchical Kriging (HK) is incorporated. The HK predictor for the efficiency of the 3-stage compressor with 144 variables is built in 16.71 s with acceptable accuracy. Remarkable improvements over the two objectives of the 3-stage compressor with 144 variables are achieved simultaneously within 870 high-fidelity CFD simulations.

关键词： multi-objective bayesian optimization multi-fidelity surrogate Parallel computing multi-stage axial flow compressor

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A general multi-objective bayesian optimization framework for the design of hybrid schemes towards adaptive complex flow simulations

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JOURNAL OF COMPUTATIONAL PHYSICS 2024年 510卷

作者： Feng, Yiqi Winter, Josef Adams, Nikolaus A. Schranner, Felix S. Tech Univ Munich Chair Aerodynam & Fluid Mech Sch Engn & Design Boltzmannstr 15 D-85748 Garching Germany Tech Univ Munich Munich Inst Integrated Mat Energy & Proc Engn MEP Lichtenbergstr 4a D-85748 Garching Germany Tech Univ Munich Sch Engn & Design Engn Risk Anal Grp Theresienstr 90 D-80333 Munich Germany

Achieving accuracy with underresolved simulation of complex compressible flows with multiscale flow structures is a challenge. Either the numerical dissipation or the resolution and thereby the numerical cost is impractically high. Also, in the design of numerical solvers, the application of a solver for specific flow classes is balanced by robustness allowing the study of a broad range of flows. In this study, we propose a hybrid fifth -order targeted essentially non -oscillatory (TENO5)based scheme tailored to optimally simulate compressible flows with underresolved dilatational and vortical multiscale structures. For optimal design, three data -driven objectives are defined. A novel objective that derives from the numerical dissipation rate analysis is a key element to deal with underresolved complex flows in practical applications. The optimization process employs a multi -objective bayesian optimization framework with an expected hypervolume improvement and three flow configurations representative for a broad range of two- and three-dimensional flows with genuine and non -genuine subgrid scales. The optimized hybrid scheme is validated by comparing with shock -capturing schemes of the weighted essentially non -oscillatory (WENO)- and TENO- families with flows of complex shock interactions, Kelvin -Helmholtz instabilities, shockvortex interactions, vortical and turbulent flows

关键词： Hybrid scheme multi-objective bayesian optimization Dispersion-dissipation relation Implicit large eddy simulation Complex flow simulations

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A Nested Weighted Tchebycheff multi-objective bayesian optimization Approach for Flexibility of Unknown Utopia Estimation in Expensive Black-Box Design Problems

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JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING 2023年第1期23卷 014501-014501页

作者： Biswas, Arpan Fuentes, Claudio Hoyle, Christopher Oregon State Univ Dept Mech Engn Corvallis OR 97331 USA Oak Ridge Natl Lab Ctr Nanophase Mat Sci Oak Ridge TN 37831 USA Oregon State Univ Dept Stat Corvallis OR 97331 USA

We propose a nested weighted Tchebycheff multi-objective bayesian optimization (WTB MOBO) framework where we built a regression model selection procedure from the ensemble of models, toward better estimation of the uncertain parameters (utopia) of the weighted Tchebycheff expensive black-box multi-objective function. In our previous work, a weighted Tchebycheff MOBO approach has been demonstrated which attempts to estimate the model parameters (utopia) in formulating the acquisition function of the weighted Tchebycheff multi-objective black-box functions, through calibration using an a priori selected regression model. However, the existing MOBO model lacks flexibility in selecting the appropriate regression models given the guided sampled data and, therefore, can under-fit or over-fit as the iterations of the MOBO progress. This ultimately can reduce the overall MOBO performance. As, in general, it is too complex to a priori guarantee a best model, this motivates us to consider a portfolio of different families (simple-to-complex) of predictive models that have been fitted with current training data guided by the WTB MOBO, and the best model is selected following a user-defined prediction root-mean-square error-based approach. The proposed approach is implemented in optimizing a thin tube design under constant loading of temperature and pressure, minimizing the risk of creep-fatigue failure and design cost. Finally, the nested WTB MOBO model performance is compared with different MOBO frameworks with respect to accuracy in parameter estimation, Pareto-optimal solutions, and function evaluation cost. This approach is generalized enough to consider different families of predictive models in the portfolio for best model selection, where the overall design architecture allows for solving any high-dimensional (multiple functions) complex black-box problems and can be extended to any other global criterion multi-objective optimization methods where prior knowledge of uto

关键词： multi-objective bayesian optimization nested design weighted techbycheff surrogate modeling model parameter estimation machine learning for engineering applications

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multi-objective bayesian modeling and optimization of 3D printing process via experimental data-driven method

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2024年第4期40卷 2096-2115页

作者： Ding, Chunfeng Wang, Jianjun Ma, Yan Tu, Yiliu (Pual) Ma, Yizhong Nanjing Univ Sci & Technol Sch Econ & Management Dept Management Sci & Engn Nanjing Peoples R China Nanjing Univ Sci & Technol Jiangsu Prov Engn Res Ctr Qual Improvement High En Nanjing Peoples R China Nanjing Univ Finance & Econ Sch Management Sci & Engn Nanjing Peoples R China Univ Calgary SCHULICH Sch Engn Dept Mech & Mfg Engn Calgary AB Canada Nanjing Univ Sci & Technol Dept Management Sci & Engn Nanjing 210018 Jiangsu Peoples R China

The instability of product quality and low printing efficiency are the main obstacles to the widespread application of 3D printing in the manufacturing industry. Optimizing printing parameters can substantially improve product quality and printing efficiency. However, existing methods for optimizing process parameters primarily rely on computationally expensive numerical simulations or costly physical experiments, which cannot balance model accuracy and experiment cost. To the best of our knowledge, almost no relevant papers have been found to address the issues of product quality and printing efficiency in 3D printing from experimental data-driven perspective. In this paper, we propose a method that integrates multiobjective bayesian optimization (MOBO) with experimental data-driven, aiming at obtaining more accurate optimization results at a lower cost. Distinguishing from previous studies, the proposed method utilizes experimental data instead of predicted values to update the model and find the optimal process parameters based on expected hypervolume improvement. The results of the 3D printing case study show that the proposed method can better model and optimize the highly fluctuating 3D printing process and obtain the optimal process parameters at a much lower cost. In addition, confirmatory experiments verify that the proposed method achieves higher printing efficiency while maintaining product quality.

关键词： additive manufacturing data-driven multi-objective bayesian optimization printing efficiency product quality

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multi-objective bayesian optimization of super hydrophobic coatings on asphalt concrete surfaces

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2019年第4期6卷 693-704页

作者： Nahvi, Ali Sadoughi, Mohammad Kazem Arabzadeh, Ali Sassani, Alireza Hu, Chao Ceylan, Halil Kim, Sunghwan Iowa State Univ Civil Construct & Environm Engn 176 Town Engn Bldg Ames IA 50011 USA Iowa State Univ Mech Engn Black Engn Bldg Ames IA 50011 USA Iowa State Univ Mech Engn Elect & Comp Engn Courtesy 2026 Black Engn Bldg Ames IA 50011 USA Iowa State Univ Construct & Environm Engn FAA PEGASAS Partnership Enhance Gen Aviat Safety Ctr Excellence COE Gen Aviat 406 Town Engn Bldg Ames IA 50011 USA Iowa State Univ Program Sustainable Pavement Engn & Res PROSPER 406 Town Engn Bldg Ames IA 50011 USA Iowa State Univ Inst Transportat 24 Town Engn Bldg Ames IA 50011 USA

Conventional snow removal strategies add direct and indirect expenses to the economy through profit lost due to passenger delays costs, pavement durability issues, contaminating the water runoff, and so on. The use of superhydrophobic (super-water-repellent) coating methods is an alternative to conventional snow and ice removal practices for alleviating snow removal operations issues. As an integrated experimental and analytical study, this work focused on optimizing superhydrophobicity and skid resistance of hydrophobic coatings on asphalt concrete surfaces. A layer-by-layer (LBL) method was utilized for spray depositing polytetrafluoroethylene (PTFE) on an asphalt concrete at different spray times and variable dosages of PTFE. Water contact angle and coefficient of friction at the microtexture level were measured to evaluate superhydrophobicity and skid resistance of the coated asphalt concrete. The optimum dosage and spay time that maximized hydrophobicity and skid resistance of flexible pavement while minimizing cost were estimated using a multi-objective bayesian optimization (BO) method that replaced the more costly experimental procedure of pavement testing with a cheap-to-evaluate surrogate model constructed based on kriging. In this method, the surrogate model is iteratively updated with new experimental data measured at proper input settings. The result of proposed optimization method showed that the super water repellency and coefficient of friction were not uniformly increased for all the specimens by increasing spray time and dosage. In addition, use of the proposed multi-objective BO method resulted in hydrophobicity and skid resistance being maximally augmented by approximately 23% PTFE dosage at a spray time of 5.5 s. (C) 2018 Society for Computational Design and Engineering. Publishing Services by Elsevier.

关键词： Sustainable airfield pavement Superhydrophobic coating Polytetrafluoroethylene Surrogate modeling multi-objective bayesian optimization

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Analysis of multi-objective bayesian optimization Using Random Scalarizations for Correlated Observations

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SN Computer Science 2021年第3期2卷 214页

作者： Ohno, Hiroshi Toyota Central R&D Labs. Inc. 41-1 Yokomichi Aichi Nagakute 480-1192 Japan

bayesian optimization (BO) has been used for a wide range of applications. However, multi-objectives (e.g., time and cost of moving people and/or products) are often found in practical situations, and thus, multi-objective optimization is often required. In addition, correlated observations are found in various applications, e.g., materials discovery. To deal with such situations effectively, random scalarizations and vector-valued Gaussian processes (GPs) are adopted. For multi-objective BO (MOBO) using random scalarizations (linear and Tchebychev schemes) and vector-valued GPs, regret bounds are analyzed. In terms of Tchebychev scalarizations, these are sub-linear in the number of rounds. Bayes risk decomposition of the GP upper confidence bound (UCB) is utilized for theoretical analysis. In addition, a weight distribution for scalarizing objective functions is estimated by nonstationary Thompson sampling where the Dirichlet distribution is a prior, thereby achieving automatic weight adaptations according to the progress of optimization. Experimental results generated from 10 benchmark functions and one real application (a hydrogen storage material database) testify to the effectiveness of using vector-valued GPs compared to real-valued GPs and the weight adaptations. Moreover, under very noisy observations in the experimental settings, real-valued GPs are found to work better than vector-valued GPs in the context of MOBO convergence. This is because of random fluctuation in posterior variance caused by noisy observations in the UCB. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. part of Springer Nature.

关键词： multi-objective bayesian optimization Random scalarizations Regret bounds Sub-linearity Thompson sampling Vector-valued Gaussian processes Weight adaptation

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