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Application of data driven optimization for Change Detection in Synthetic Aperture Radar Images

IEEE ACCESS 2020年 8卷 11426-11436页

作者： Li, Yangyang Liu, Guangyuan Li, Tiantian Jiao, Licheng Lu, Gao Marturi, Naresh Xidian Univ Sch Artificial Intelligence Key Lab Intelligent Percept & Image Understanding Minist Educ Xian 710071 Peoples R China HUAWEI Technol Co Ltd Shanghai Peoples R China Univ Birmingham Extreme Robot Lab Edgbaston B15 2TT England

data-driven optimization is an efficient global optimization algorithm for expensive black-box functions. In this paper, we apply data-driven optimization algorithm to the task of change detection with synthetic aperture radar (SAR) images for the first time. We first propose an easy-to-implement threshold algorithm for change detection in SAR images based on data-driven optimization. Its performance has been compared with commonly used methods like generalized Kittler and Illingworth threshold algorithms (GKIT). Next, we demonstrate how to tune the hyper-parameter of a (previously available) deep belief network (DBN) for change detection using data-driven optimization. Extensive evaluations are carried out using publicly available benchmark datasets. The obtained results suggest comparatively strong performance of our optimized DBN-based change detection algorithm.

关键词： Hyper-parameter optimization data-driven optimization change detection deep belief network (DBN) synthetic aperture radar (SAR) image

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data-driven Evolutionary optimization: An Overview and Case Studies

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2019年第3期23卷 442-458页

作者： Jin, Yaochu Wang, Handing Chugh, Tinkle Guo, Dan Miettinen, Kaisa Univ Surrey Dept Comp Sci Guildford GU2 7XH Surrey England Taiyuan Univ Sci & Technol Dept Comp Sci & Technol Taiyuan 030024 Shanxi Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Liaoning Peoples R China Xidian Univ Sch Artificial Intelligence Xian 710071 Shaanxi Peoples R China Univ Exeter Dept Comp Sci Exeter EX4 4QD Devon England Univ Jyvaskyla Fac Informat Technol FI-40014 Jyvaskyla Finland

Most evolutionary optimization algorithms assume that the evaluation of the objective and constraint functions is straightforward. In solving many real-world optimization problems, however, such objective functions may not exist. Instead, computationally expensive numerical simulations or costly physical experiments must be performed for fitness evaluations. In more extreme cases, only historical data are available for performing optimization and no new data can be generated during optimization. Solving evolutionary optimization problems driven by data collected in simulations, physical experiments, production processes, or daily life are termed data-driven evolutionary optimization. In this paper, we provide a taxonomy of different data driven evolutionary optimization problems, discuss main challenges in data-driven evolutionary optimization with respect to the nature and amount of data, and the availability of new data during optimization. Real-world application examples are given to illustrate different model management strategies for different categories of data-driven optimization problems.

关键词： data science data-driven optimization evolutionary algorithms (EAs) machine learning model management surrogate

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data-driven stochastic optimization approaches to determine decision thresholds for risk estimation models

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IISE TRANSACTIONS 2020年第10期52卷 1098-1121页

作者： Garcia, Gian-Gabriel P. Lavieri, Mariel S. Jiang, Ruiwei McCrea, Michael A. McAllister, Thomas W. Broglio, Steven P. Univ Michigan Dept Ind & Operat Engn Ann Arbor MI 48109 USA Med Coll Wisconsin Dept Neurosurg Res Milwaukee WI 53226 USA Med Coll Wisconsin Dept Neurol Milwaukee WI 53226 USA Indiana Univ Sch Med Dept Psychiat Indianapolis IN 46202 USA Univ Michigan Sch Kinesiol Ann Arbor MI 48109 USA

The increasing availability of data has popularized risk estimation models in many industries, especially healthcare. However, properly utilizing these models for accurate diagnosis decisions remains challenging. Our research aims to determine when a risk estimation model provides sufficient evidence to make a positive or negative diagnosis, or if the model is inconclusive. We formulate the Two-Threshold Problem (TTP) as a stochastic program which maximizes sensitivity and specificity while constraining false-positive and false-negative rates. We characterize the optimal solutions to TTP as either two-threshold or one-threshold and show that its optimal solution can be derived from a related linear program (TTP*). We also derive utility-based and multi-class classification frameworks for which our analytical results apply. We solve TTP* using data-driven methods: quantile estimation (TTP*-Q) and distributionally robust optimization (TTP*-DR). Through simulation, we characterize the feasibility, optimality, and computational burden of TTP*-Q and TTP*-DR and compare TTP*-Q to an optimized single threshold. Finally, we apply TTP* to concussion assessment data and find that it achieves greater accuracy at lower misclassification rates compared with traditional approaches. This data-driven framework can provide valuable decision support to clinicians by identifying "easy" cases which can be diagnosed immediately and "hard" cases which may require further evaluation before diagnosing.

关键词： data-driven optimization stochastic programming distributionally robust optimization quantile estimation diagnosis decisions risk estimation models

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A data Stream Ensemble Assisted Multifactorial Evolutionary Algorithm for Offline data-driven Dynamic optimization

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EVOLUTIONARY COMPUTATION 2023年第4期31卷 433-458页

作者： Yang, Cuie Ding, Jinliang Jin, Yaochu Chai, Tianyou Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Bielefeld Univ D-33619 Bielefeld Germany

Existing work on offline data-driven optimization mainly focuses on problems in static environments, and little attention has been paid to problems in dynamic environments. Offline data-driven optimization in dynamic environments is a challenging problem because the distribution of collected data varies over time, requiring surrogate models and optimal solutions tracking with time. This paper proposes a knowledge-transfer-based data-driven optimization algorithm to address these issues. First, an ensemble learning method is adopted to train surrogate models to leverage the knowledge of data in historical environments as well as adapt to new environments. Specifically, given data in a new environment, a model is constructed with the new data, and the preserved models of historical environments are further trained with the new data. Then, these models are considered to be base learners and combined as an ensemble surrogate model. After that, all base learners and the ensemble surrogate model are simultaneously optimized in a multitask environment for finding optimal solutions for real fitness functions. In this way, the optimization tasks in the previous environments can be used to accelerate the tracking of the optimum in the current environment. Since the ensemble model is the most accurate surrogate, we assign more individuals to the ensemble surrogate than its base learners. Empirical results on six dynamic optimization benchmark problems demonstrate the effectiveness of the proposed algorithm compared with four state-of-the-art offline data-driven optimization algorithms. Code is available at https://***/Peacefulyang/DSE_***.

关键词： data-driven optimization multitask evolutionary optimization dynamic optimization

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Social Learning Particle Swarm optimization with Two-surrogate Collaboration for Offline data-driven Multiobjective optimization

Social Learning Particle Swarm Optimization with Two-surroga...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Yang, Qi-Te Zhan, Zhi-Hui Li, Yun Zhang, Jun South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Univ Elect Sci & Technol China Shenzhen Inst Adv Study Shenzhen 518110 Peoples R China Hanyang Univ Ansan 15588 South Korea

ISBN: (纸本)9781450392372

data-driven evolutionary algorithms (DDEAs) have shown strong capacity in solving optimization problems with the help of surrogate models. However, current DDEAs often fall into the trap of inaccurate surrogates if no additional data can be obtained for the surrogates update during the optimization process. In addition, when dealing with multiobjective optimization problems, the surrogates in DDEAs will further suffer the difficulty of aggravating cumulative predicted fitness error. To overcome these difficulties, we propose a two-surrogate collaboration (TSC) management method, in which a Kriging surrogate and a radial basis function network (RBFN) surrogate are adopted to approximate the real fitness evaluation for the parent solutions and the newly generated offspring solutions, respectively. This TSC management method can effectively reduce the prediction uncertainty and improve the prediction performance of each surrogate without any other surrogate update process. During the optimization process, we use a modified social learning particle swarm optimization (SLPSO) as the basic search method and propose an offline DDEA. With the help of TSC, our resulting SLPSO-TSC algorithm can search for potential optimal solutions quickly and effectively without the help of any real fitness evaluation during the optimization process. The performance of the proposed SLPSO-TSC algorithm is verified on eleven widely used benchmark problems and compared with five different offline DDEAs. The experimental results show the high competitiveness of our proposed SLPSO-TSC for offline data-driven multiobjective optimization.

关键词： Particle swarm optimization data-driven optimization multiobjective optimization surrogate-assisted optimization

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Consensus Modeling with Asymmetric Cost Based on data-driven Robust optimization

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GROUP DECISION AND NEGOTIATION 2021年第6期30卷 1395-1432页

作者： Qu, Shaojian Han, Yefan Wu, Zhong Raza, Hassan Nanjing Univ Informat Sci & Technol Nanjing 210044 Peoples R China Univ Shanghai Sci & Technol Shanghai 200093 Peoples R China

The robust optimization method has progressively become a research hot spot as a valuable means for dealing with parameter uncertainty in optimization problems. Based on the asymmetric cost consensus model, this paper considers the uncertainties of the experts' unit adjustment costs under the background of group decision making. At the same time, four uncertain level parameters are introduced. For three types of minimum cost consensus models with direction restrictions, including MCCM-DC,epsilon-MCCM-DC and threshold-based (TB)-MCCM-DC, the robust cost consensus models corresponding to four types of uncertainty sets (Box set, Ellipsoid set, Polyhedron set and Interval-Polyhedron set) are established. Sensitivity analysis is carried out under different parameter conditions to determine the robustness of the solutions obtained from robust optimization models. The robust optimization models are then compared to the minimum cost models for consensus. The example results show that the Interval-Polyhedron set's robust models have the smallest total costs and strongest robustness. Decision makers can choose the combination of uncertainty sets and uncertain levels according to their risk preferences to minimize the total cost. Finally, in order to reduce the conservatism of the classical robust optimization method, the pricing information of the new product MACUBE 550 is used to build a data-driven robust optimization model. Ellipsoid uncertainty set is proved to better trade-off the average performance and robust performance through different measurement indicators. Therefore, the uncertainty set can be selected according to the needs of the group.

关键词： Group decision making Minimum cost consensus Robust optimization data-driven optimization Asymmetric cost

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Ride-Sharing Matching under Travel Time Uncertainty through A data-driven Robust optimization Approach

Ride-Sharing Matching under Travel Time Uncertainty through ...

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IEEE Intelligent Transportation Systems Conference (ITSC)

作者： Li, Xiaoming Gao, Jie Wang, Chun Huang, Xiao Nie, Yimin Concordia Univ Concordia Inst Informat Syst Engn CIISE Montreal PQ H3G 1M8 Canada Shenyang Aerosp Univ Sch Comp Shenyang 110136 Peoples R China Ericsson INC Global Artificial Intelligence Accelerator GALA I Montreal PQ H4R 2A4 Canada Concordia Univ Concordia John Molson Sch Business JMSB Montreal PQ H3G 1M8 Canada

ISBN: (纸本)9781728191423

In this paper, we propose a data-driven robust optimization model to reduce total travel cost in ride-sharing systems under travel time uncertainty. Instead of using a pre-defined uncertainty set, we study a data-driven robust optimization approach that integrates gated recurrent units (GRUs) predictions with a one-stage robust optimization model. The proposed approach has the ability to compute high quality solutions by leveraging the large-scale historical data to derive the uncertainty set for the designed robust optimization model. To evaluate the proposed approach, we conduct a group of simulations based on the New York taxi trip record data sets. The validation results show that our data-driven robust optimization approach outperforms the robust optimization approach with a pre-defined uncertainty set in terms of travellers' total travel cost. Most importantly, the total travel cost under the proposed approach is reduced by up to 31.7%, and by 26.7% on average compared with the robust model with a pre-defined uncertainty set.

关键词： data-driven optimization robust optimization gated recurrent units ride-sharing

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Personalized data-driven Learning and optimization: Theory and Applications to Healthcare

Personalized Data-Driven Learning and Optimization: Theory a...

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作者： Keyvanshokooh, Esmaeil University of Michigan

学位级别：Ph.D., Doctor of Philosophy

This dissertation is broadly about developing new personalized data-driven learning and optimization methods with theoretical performance guarantees for three important applications in healthcare operations management and medical decision-making. In these research problems, we are dealing with longitudinal settings, where the decision-maker needs to make multi-stage personalized decisions while collecting data in-between stages. In each stage, the decision-maker incorporates the newly observed data in order to update his current system's model or belief, thereby making better decisions next. This new class of data-driven learning and optimization methods indeed learns from data over time so as to make efficient and effective decisions for each individual in real-time under dynamic, uncertain environments. The theoretical contributions lie in the design and analysis of these new predictive and prescriptive learning and optimization methods and proving theoretical performance guarantees for them. The practical contributions are to apply these methods to resolve unmet real-world needs in healthcare operations management and medical decision-making so as to yield managerial and practical insights and new functionality.

关键词： Machine learning data-driven optimization Online learning and multi-armed bandit Personalized medicine Healthcare operations Online convex optimization

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Machine learning enabled uncertainty set for data-driven robust optimization

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JOURNAL OF PROCESS CONTROL 2024年 144卷

作者： Li, Yun Yorke-Smith, Neil Keviczky, Tamas Delft Univ Technol Delft Ctr Syst & Control Delft Netherlands Delft Univ Technol Algorithm Grp Delft Netherlands

The way how the uncertainties are represented by sets plays a vital role in the performance of robust optimization (RO). This paper presents a novel approach leveraging machine learning (ML) techniques to construct data-driven uncertainty sets from historical uncertainty data for RO problems. The proposed method integrates Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Gaussian Mixture Model (GMM), and Principle Component Analysis (PCA) systematically to eliminate the influence of uncertainty scenarios with low occurrence probability and generate a nonconvex uncertainty set that is a union of multiple basic subsets (box or ellipsoid) without sacrificing its computational tractability. In addition to presenting a comprehensive algorithm for uncertainty set development, this paper offers detailed guidelines for parameter tuning and performance analysis. By harnessing the well-established ML packages scikitlearn, a Python-based toolkit for implementing the proposed approach is also provided. Furthermore, a computationally efficient solution fora two-stage linear RO problem with the proposed data-driven uncertainty set is derived, alongside establishing a probabilistic guarantee of constraint satisfaction for out-of-sample uncertainties. Extensive numerical experiments, conducted on both synthetic and real-world datasets as well as an optimization-based control problem, are performed to demonstrate the efficacy of the proposed methodology.

关键词： data-driven optimization Uncertainty set Robust optimization Machine learning

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Privacy-preserving energy trading management in networked microgrids via data-driven robust optimization assisted by machine learning

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SUSTAINABLE ENERGY GRIDS & NETWORKS 2023年 34卷

作者： Mohseni, Shayan Pishvaee, Mir Saman Dashti, Reza Iran Univ Sci & Technol Sch Ind Engn Tehran Iran Iran Univ Sci & Technol Sch Adv Technol Tehran Iran

Trading mechanism design, uncertainty treatment and privacy protection are the main issues in the energy management of networked microgrids (MGs). To address these issues in a comprehensive manner, this paper proposes a data-driven two-level transactive energy management framework, where the upper-level determines the optimal strategies of internal scheduling within MGs and external trading between MGs while the lower-level formulates a Nash bargaining game model for the fair allocation of trading benefits. The uncertainties of renewable energy sources are fully captured by an adjustable data-driven robust optimization approach with an uncertainty set constructed using the robust kernel density estimation (RKDE) as a machine learning technique. The resulting uncertainty set can provide robust scheduling and trading schemes even when the power generation data of wind turbines and photovoltaic systems are contaminated with anomalous samples, whereas conventional sets are not reliable in the case of contaminated data. To preserve the operational independence and information privacy of MGs, the proposed model is solved in a distributed manner by the alternating direction method of multipliers (ADMM) and the augmented Lagrange-based alternating direction inexact Newton (ALADIN) algorithms. ADMM is commonly used in previous studies, but it has low computational efficiency for handling the consensus process among a large number of MGs. This paper applies ADMM and ALADIN to enhance the applicability of the proposed model when the size and complexity of the networks increase. Numerical tests show the effectiveness of the proposed framework and solution methodology in terms of system cost, solution robustness, and convergence speed. (c) 2023 Elsevier Ltd. All rights reserved.

关键词： data-driven optimization Peer-to-peer energy trading Nash bargaining game ADMM Distributed optimization

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