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Even Search in a Promising Region for Constrained Multi-Objective Optimization

IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 474-486页

作者： Fei Ming Wenyin Gong yaochu jin School of Computer Science China University of GeosciencesWuhan 430074China IEEE Faculty of Technology Bielefeld UniversityNorth Rhine-Westphalia 33619Germany School of Engineering Westlake UniversityHangzhou 310030China

In recent years, a large number of approaches to constrained multi-objective optimization problems(CMOPs) have been proposed, focusing on developing tweaked strategies and techniques for handling constraints. However, an overly finetuned strategy or technique might overfit some problem types,resulting in a lack of versatility. In this article, we propose a generic search strategy that performs an even search in a promising region. The promising region, determined by obtained feasible non-dominated solutions, possesses two general ***, the constrained Pareto front(CPF) is included in the promising region. Second, as the number of feasible solutions increases or the convergence performance(i.e., approximation to the CPF) of these solutions improves, the promising region shrinks. Then we develop a new strategy named even search,which utilizes the non-dominated solutions to accelerate convergence and escape from local optima, and the feasible solutions under a constraint relaxation condition to exploit and detect feasible regions. Finally, a diversity measure is adopted to make sure that the individuals in the population evenly cover the valuable areas in the promising region. Experimental results on 45 instances from four benchmark test suites and 14 real-world CMOPs have demonstrated that searching evenly in the promising region can achieve competitive performance and excellent versatility compared to 11 most state-of-the-art methods tailored for CMOPs.

关键词： Constrained multi-objective optimization even search evolutionary algorithms promising region real-world problems

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Constrained Multi-Objective Optimization With Deep Reinforcement Learning Assisted Operator Selection

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 919-931页

作者： Fei Ming Wenyin Gong Ling Wang yaochu jin the School of Computer Science China University of GeosciencesWuhan 430074China the Department of Automation Tsinghua UniversityBeijing 100084China the Faculty of Technology Bielefeld UniversityNorth Rhine-Westphalia33619 BielefeldGermany

Solving constrained multi-objective optimization problems with evolutionary algorithms has attracted considerable *** constrained multi-objective optimization evolutionary algorithms(CMOEAs)have been developed with the use of different algorithmic strategies,evolutionary operators,and constraint-handling *** performance of CMOEAs may be heavily dependent on the operators used,however,it is usually difficult to select suitable operators for the problem at ***,improving operator selection is promising and necessary for *** work proposes an online operator selection framework assisted by Deep Reinforcement *** dynamics of the population,including convergence,diversity,and feasibility,are regarded as the state;the candidate operators are considered as actions;and the improvement of the population state is treated as the *** using a Q-network to learn a policy to estimate the Q-values of all actions,the proposed approach can adaptively select an operator that maximizes the improvement of the population according to the current state and thereby improve the algorithmic *** framework is embedded into four popular CMOEAs and assessed on 42 benchmark *** experimental results reveal that the proposed Deep Reinforcement Learning-assisted operator selection significantly improves the performance of these CMOEAs and the resulting algorithm obtains better versatility compared to nine state-of-the-art CMOEAs.

关键词： Constrained multi-objective optimization deep Qlearning deep reinforcement learning(DRL) evolutionary algorithms evolutionary operator selection

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Deep Industrial Image Anomaly Detection: A Survey

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Machine Intelligence Research 2024年第1期21卷 104-135页

作者： Jiaqi Liu Guoyang Xie jinbao Wang Shangnian Li Chengjie Wang Feng Zheng yaochu jin Research Institute of Trustworthy Autonomous Systems Southern University of Science and TechnologyShenzhen 518055China NICEGroup Universityof SurreyGuildford GU27YXUK Youtu Lab TencentShanghai 200233China NICE Group Bielefeld UniversityBielefeld 33619Germany

The recent rapid development of deep learning has laid a milestone in industrial image anomaly detection(IAD).In this pa-per,we provide a comprehensive review of deep learning-based image anomaly detection techniques,from the perspectives of neural net-work architectures,levels of supervision,loss functions,metrics and *** addition,we extract the promising setting from indus-trial manufacturing and review the current IAD approaches under our proposed ***,we highlight several opening chal-lenges for image anomaly *** merits and downsides of representative network architectures under varying supervision are ***,we summarize the research findings and point out future research *** resources are available at https://***/M-3LAB/awesome-industrial-anomaly-detection.

关键词： Image anomaly detection defect detection industrial manufacturing deep learning computer vision

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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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GPU加速的演化算法求解多目标流水车间调度问题

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计算机应用 2024年第5期44卷 1364-1371页

作者：姜涛梁振宇程然金耀初南方科技大学计算机科学与工程系广东深圳518055 鹏城实验室广东深圳518055 西湖大学杭州310012

智能制造和环境可持续性研究中,多目标调度问题对于协调生产效率、成本管理与环境保护之间的平衡具有至关重要的意义,但现有基于CPU的调度解决方案在处理大规模生产任务时仍面临效率和时效性的限制,而GPU的并行计算能力可为优化大规模... 详细信息

智能制造和环境可持续性研究中,多目标调度问题对于协调生产效率、成本管理与环境保护之间的平衡具有至关重要的意义,但现有基于CPU的调度解决方案在处理大规模生产任务时仍面临效率和时效性的限制,而GPU的并行计算能力可为优化大规模流水车间调度问题提供新的解决途径。针对多目标零等待流水车间调度问题(NWFSP),以同时最小化最大完成时间和总能耗(TEC)为优化目标,构建了混合整数线性规划模型(MILP)表征该调度问题,并提出一种基于GPU加速的张量化演化算法(Tensor-GPU-NSGA-Ⅱ)求解该问题。Tensor-GPU-NSGA-Ⅱ的主要创新在于对NWFSP关于最小化最大完成时间和TEC的计算过程的张量化处理,并提出了一种基于GPU的并行种群更新方法。实验结果表明,在500工件和20机器的问题规模下,Tensor-GPU-NSGA-Ⅱ在计算效率上相较于传统NSGA-Ⅱ算法取得了9761.75的加速比;且随着种群规模的增加,它的加速性能有显著提升。

关键词：智能制造多目标优化流水车间调度 GPU加速张量化方法

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Even Search in a Promising Region for Constrained Multi-Objective Optimization

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自动化学报（英文版） 2024年第2期

作者： Fei Ming Wenyin Gong yaochu jin

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A Survey of Evolutionary Algorithms for Multi-Objective Optimization Problems With Irregular Pareto Fronts

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IEEE/CAA Journal of Automatica Sinica 2021年第2期8卷 303-318,I0001-I0004页

作者： Yicun Hua Qiqi Liu Kuangrong Hao yaochu jin the College of Information Sciences and Technology Donghua UniversityShanghai 201620China the Department of Computer Science University of SurreyGuildfordSurrey GU27XHU.K.

Evolutionary algorithms have been shown to be very successful in solving multi-objective optimization problems(MOPs).However,their performance often deteriorates when solving MOPs with irregular Pareto *** remedy this issue,a large body of research has been performed in recent years and many new algorithms have been *** paper provides a comprehensive survey of the research on MOPs with irregular Pareto *** start with a brief introduction to the basic concepts,followed by a summary of the benchmark test problems with irregular problems,an analysis of the causes of the irregularity,and real-world optimization problems with irregular Pareto ***,a taxonomy of the existing methodologies for handling irregular problems is given and representative algorithms are reviewed with a discussion of their strengths and ***,open challenges are pointed out and a few promising future directions are suggested.

关键词： Evolutionary algorithm machine learning multi-objective optimization problems(MOPs) irregular Pareto fronts

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Incremental data-driven optimization of complex systems in nonstationary environments

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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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A comprehensive survey of robust deep learning in computer vision

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Journal of Automation and Intelligence 2023年第4期2卷 175-195页

作者： Jia Liu yaochu jin Ping An Property&Casualty Insurance Company Shenzhen518048GuangdongChina School of Engineering Westlake UniversityHangzhou310030China

Deep learning has presented remarkable progress in various *** the excellent performance,deep learning models remain not robust,especially to well-designed adversarial examples,limiting deep learning models employed in security-critical ***,how to improve the robustness of deep learning has attracted increasing attention from *** paper investigates the progress on the threat of deep learning and the techniques that can enhance the model robustness in computer *** previous relevant survey papers summarizing adversarial attacks and defense technologies,this paper also provides an overview of the general robustness of deep ***,this survey elaborates on the current robustness evaluation approaches,which require further *** paper also reviews the recent literature on making deep learning models resistant to adversarial examples from an architectural perspective,which was rarely mentioned in previous ***,interesting directions for future research are listed based on the reviewed *** survey is hoped to serve as the basis for future research in this topical field.

关键词： Robustness Deep learning Computer vision Survey Adversarial attack Adversarial defenses

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Integrating Conjugate Gradients Into Evolutionary Algorithms for Large-Scale Continuous Multi-Objective Optimization

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IEEE/CAA Journal of Automatica Sinica 2022年第10期9卷 1801-1817页

作者： Ye Tian Haowen Chen Haiping Ma Xingyi Zhang Kay Chen Tan yaochu jin the Information Materials and Intelligent Sensing Laboratory of Anhui Province Institutes of Physical Science and Information TechnologyAnhui UniversityHefei 230601China the Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefei 230601China IEEE the Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial IntelligenceAnhui UniversityHefei 230601China the Department of Computing The Hong Kong Polytechnic UniversityHong Kong SARChina the Faculty of Technology Bielefeld UniversityBielefeld 33619Germany

Large-scale multi-objective optimization problems(LSMOPs)pose challenges to existing optimizers since a set of well-converged and diverse solutions should be found in huge search *** evolutionary algorithms are good at solving small-scale multi-objective optimization problems,they are criticized for low efficiency in converging to the optimums of *** contrast,mathematical programming methods offer fast convergence speed on large-scale single-objective optimization problems,but they have difficulties in finding diverse solutions for ***,how to integrate evolutionary algorithms with mathematical programming methods to solve LSMOPs remains *** this paper,a hybrid algorithm is tailored for LSMOPs by coupling differential evolution and a conjugate gradient *** the one hand,conjugate gradients and differential evolution are used to update different decision variables of a set of solutions,where the former drives the solutions to quickly converge towards the Pareto front and the latter promotes the diversity of the solutions to cover the whole Pareto *** the other hand,objective decomposition strategy of evolutionary multi-objective optimization is used to differentiate the conjugate gradients of solutions,and the line search strategy of mathematical programming is used to ensure the higher quality of each offspring than its *** comparison with state-of-the-art evolutionary algorithms,mathematical programming methods,and hybrid algorithms,the proposed algorithm exhibits better convergence and diversity performance on a variety of benchmark and real-world LSMOPs.

关键词： Conjugate gradient differential evolution evolutionary computation large-scale multi-objective optimization mathematical programming

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