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Incremental data-driven optimization of complex systems in nonstationary environments

Science China(Information Sciences) 2018年第12期61卷 211-213页

作者： Cuie YANG Jinliang DING Yaochu JIN Tianyou CHAI State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Department of Computer Science University of Surrey

Dear editor,In a complex system, many real-world optimization problems do not have explicit optimization functions or constraint functions, whereas only data from production processes are available because of the complexity of the system [1]. Thus,optimization in this scenario can generally rely on collected historical datasets, and these problems are also known as offline data-driven optimization

关键词： knowledge transfer Nonstationary Environments Surrogate Model Complex Systems data-driven optimization

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可信联邦学习进化优化算法综述

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Engineering 2024年第3期34卷 23-42页

作者： Qiqi Liu Yuping Yan Yaochu Jin Xilu Wang Peter Ligeti Guo Yu Xueming Yan School of Engineering Westlake UniversityHangzhou 310030China Faculty of Informatics Department of Computer AlgebraEötvös Loránd UniversityBudapest 1053Hungary Faculty of Technology Bielefeld UniversityBielefeld 33619Germany Institute of Intelligent Manufacturing Nanjing Tech UniversityNanjing 211816China School of Information Science and Technology Guangdong University of Foreign StudiesGuangzhou 510006China

With the development of edge devices and cloud computing,the question of how to accomplish machine learning and optimization tasks in a privacy-preserving and secure way has attracted increased attention over the past *** a privacy-preserving distributed machine learning method,federated learning(FL)has become popular in the last few ***,the data privacy issue also occurs when solving optimization problems,which has received little attention so *** survey paper is concerned with privacy-preserving optimization,with a focus on privacy-preserving data-driven evolutionary *** aims to provide a roadmap from secure privacy-preserving learning to secure privacy-preserving optimization by summarizing security mechanisms and privacy-preserving approaches that can be employed in machine learning and *** provide a formal definition of security and privacy in learning,followed by a comprehensive review of FL schemes and cryptographic privacy-preserving ***,we present ideas on the emerging area of privacy-preserving optimization,ranging from privacy-preserving distributed optimization to privacy-preserving evolutionary optimization and privacy-preserving Bayesian optimization(BO).We further provide a thorough security analysis of BO and evolutionary optimization methods from the perspective of inferring attacks and active *** the basis of the above,an in-depth discussion is given to analyze what FL and distributed optimization strategies can be used for the design of federated optimization and what additional requirements are needed for achieving these ***,we conclude the survey by outlining open questions and remaining challenges in federated data-driven *** hope this survey can provide insights into the relationship between FL and federated optimization and will promote research interest in secure federated optimization.

关键词： Federated learning Privacy-preservation Security Evolutionary optimization data-driven optimization Bayesian optimization

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Process optimization with consideration of uncertainties——An overview

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Chinese Journal of Chemical Engineering 2018年第8期26卷 1700-1706页

作者： Ying Chen Zhihong Yuan Bingzhen Chen Department of Chemical Engineering Tsinghua University

optimization under uncertainty is a challenging topic of practical importance in the Process Systems *** the solution of an optimization problem generally exhibits high sensitivity to the parameter variations, the deterministic model which neglects the parametric uncertainties is not suitable for practical applications. This paper provides an overview of the key contributions and recent advances in the field of process optimization under uncertainty over the past ten years and discusses their advantages and limitations thoroughly. The discussion is focused on three specific research areas, namely robust optimization, stochastic programming and chance constrained programming, based on which a systematic analysis of their applications, developments and future directions are presented. It shows that the more recent trend has been to integrate different optimization methods to leverage their respective superiority and compensate for their drawbacks. Moreover, data-driven optimization, which combines mathematical programming methods and machine learning algorithms, has become an emerging and competitive tool to handle optimization problems in the presence of uncertainty based on massive historical data.

关键词： optimization under uncertainty Robust optimization Stochastic programming Chance constrained programming data-driven optimization

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Multiple input self-organizing-map ResNet model for optimization of petroleum refinery conversion units

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Frontiers of Chemical Science and Engineering 2023年第6期17卷 759-771页

作者： Jiannan Zhu Vladimir Mahalec Chen Fan Minglei Yang Feng Qian Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of EducationEast China University of Science and TechnologyShanghai 200237China Department of Chemical Engineering McMaster UniversityHamiltonOntario L8S 4L8Canada Engineering Research Center of Process System Engineering Ministry of EducationEast China University of Science and TechnologyShanghai 200237China

This work introduces a deep-learning network,i.e.,multi-input self-organizing-map ResNet(MISR),for modeling refining units comprised of two reactors and a separation *** model is comprised of self-organizing-map and the neural network *** self-organizing-map part maps the input data into multiple two-dimensional planes and sends them to the neural network *** the neural network part,residual blocks enhance the convergence and accuracy,ensuring that the structure will not be overfitted *** of the MISR model of hydrocracking unit also benefits from the utilization of prior knowledge of the importance of the input variables for predicting properties of the *** results show that the proposed MISR structure predicts more accurately the product yields and properties than the previously introduced self-organizing-map convolutional neural network model,thus leading to more accurate optimization of the hydrocracker ***,the MISR model has smoother error convergence than the previous *** operating conditions have been determined via multi-round-particle-swarm and differential evolution *** experiments show that the MISR model is suitable for modeling nonlinear conversion units which are often encountered in refining and petrochemical plants.

关键词： hydrocracking convolutional neural networks self-organizing map deep learning data-driven optimization

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