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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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Proximal-exploration multi-objective bayesian optimization for inverse identification of cyclic constitutive law of structural steels

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STRUCTURAL AND multiDISCIPLINARY optimization 2022年第7期65卷 199-199页

作者： Do, Bach Ohsaki, Makoto Kyoto Univ Grad Sch Engn Dept Architecture & Architectural Engn Nishikyo Ku Kyoto 6158540 Japan

Despite its importance in seismic response analysis, solving an inverse problem to identify the cyclic elastoplastic parameters for structural steels using conventional optimization algorithms still demands a substantial computational cost of repeatedly carrying out many nonlinear analyses. The parameters are commonly identified based on experimental measures from a single loading history, leading them to be biased and giving inaccurate predictions of structural behavior under other loading histories. To address these issues, we formulate a multi-objective inverse problem that simultaneously minimizes the error functions representing the differences between simulated responses and those measured experimentally from various cyclic tests of a steel specimen or a structural component. We then propose proximal-exploration multi-objective bayesian optimization (MOBO) for solving the formulated inverse problem, resulting in an approximate Pareto front of parameters while limiting the number of costly simulations. MOBO sorts an initial Pareto front and constructs Gaussian process (GP) models for the error functions from a training dataset. It then relies on the hypervolume of the current solutions, the GP models, and a proximal exploration surrounding the current best compromise parameters to formulate an acquisition function that guides the improvement of the current solutions intelligently. Two identification examples show that the parameters obtained from the multi-objective inverse problem can reduce the bias induced using a single loading history for identification. The robustness of MOBO as well as a good prediction performance of the best compromise solution of identified parameters are demonstrated.

关键词： Elastoplastic consititutive law Parameter identification Best compromise parameters Structural steels multi-objective bayesian optimization Cyclic loading

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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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Feed formulation using multi-objective bayesian optimization

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2024年 224卷

作者： Uribe-Guerra, Gabriel D. Munera-Ramirez, Danny A. Arias-Londono, Julian D. Univ Antioquia Dept Syst Engn Intelligent Informat Syst Lab Calle 67 53-108 Medellin 050010 Colombia Univ Politecn Madrid Dept Signals Syst & Radiocommun ETSI Telecomunicac Ave Complutense 30 Madrid Spain

Animal diet design has been addressed mainly by optimizing analytical functions that describe digestible energy and essential nutrients, along with a set of restrictions regarding minimum nutritional content in the feed formulation. This approach results in limitations since theoretical models are not flexible enough to incorporate variables related to environmental or zootechnical conditions that affect production efficiency or to include multiple objectives regarding current challenges associated with the adaptability to new environmental contexts and the reduction of ecological footprint. Unlike analytical methods, heuristic approaches can deal with variables from multiple sources using surrogate data-driven models of the objectives functions but commonly require thousands of evaluations of the target function, which is unfeasible in the context of animal diet formulation. This work proposes the use of bayesian optimization as an alternative solution to address the animal diet design problem since it is intended to optimize costly-to-evaluate target functions and is able to deal with noisy sampling, which is helpful in handling the intrinsic variability in the nutrient content of raw materials. A multi-objective swine diet design problem is used to evaluate the suitability of bayesian optimization to optimize three target functions: digestible energy, lysine, and cost, and the solutions are compared with a fractional stochastic programming approach. The analytical formulation of the problem is not considered by the bayesian optimization approach, but target functions are modeled through surrogate bayesian models, where only input and output responses are used to drive the optimization process. Results show that a multi-objective bayesian optimization process is able to find better solutions than previously proposed methods, improving in 10.71%, 14.77%, and 3.79% the three objectives defined. Experiments using batches of query samples per iteration show that the o

关键词： multi-objective bayesian optimization Food production Precision agriculture Swine diet design

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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 optimization of Deep Reinforcement Learning for Environmental, Social, and Governance (ESG) Financial Portfolio Management

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Intelligent Systems in Accounting, Finance and Management 2025年第2期32卷

作者： Eduardo C. Garrido-Merchán Sol Mora-Figueroa María Coronado-Vaca Faculty of Economics and Business (ICADE) Universidad Pontificia Comillas Madrid Spain Institute for Research in Technology (IIT) Universidad Pontificia Comillas Madrid Spain

Financial portfolio management focuses on the maximization of several objectives in a trading period related not only to the risk and performance of the portfolio but also to other objectives such as the environment, social, and governance (ESG) score of the portfolio. Regrettably, classic methods such as the Markowitz model do not take into account ESG scores but only the risk and performance of the portfolio. Moreover, the assumptions made by this model about the financial returns make it unfeasible to be applicable to markets with high volatility such as the technological sector. This paper investigates the application of deep reinforcement learning (DRL) for ESG financial portfolio management. DRL agents circumvent the issue of classic models in the sense that they do not make assumptions like the financial returns being normally distributed and are able to deal with any information like the ESG score if they are configured to gain a reward that makes an objective better. However, the performance of DRL agents has high variability, and it is very sensible to the value of their hyperparameters. bayesian optimization is a class of methods that are suited to the optimization of black-box functions, that is, functions whose analytical expression is unknown and are noisy and expensive to evaluate. The hyperparameter tuning problem of DRL algorithms perfectly suits this scenario. As training an agent just for one objective is a very expensive period, requiring millions of timesteps, instead of optimizing an objective being a mixture of a risk-performance metric and an ESG metric, we choose to separate the objective and solve the multi-objective scenario to obtain an optimal Pareto set of portfolios representing the best trade-off between the Sharpe ratio and the ESG mean score of the portfolio and leaving to the investor the choice of the final portfolio. We conducted our experiments using environments encoded within the OpenAI Gym, adapted from the FinRL platform. Th

关键词： deep reinforcement learning (DRL) environmental, social, and governance (ESG) multi-objective bayesian optimization portfolio management social sustainable finance

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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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