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MOEA/D With Spatial–Temporal Topological Tensor Prediction for Evolutionary Dynamic Multiobjective optimization

IEEE Transactions on Evolutionary Computation 2025年第3期29卷 764-778页

作者： Wang, Xianpeng Zhao, Yumeng Tang, Lixin Yao, Xin Northeastern University National Frontiers Science Center for Industrial Intelligence and Systems Optimization Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Shenyang110819 China Northeastern University Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Shenyang110819 China Northeastern University National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China Lingnan University Department of Computing and Decision Sciences Hong Kong University of Birmingham School of Computer Science Birmingham United Kingdom

When solving dynamic multiobjective optimization problems, most evolutionary algorithms (EAs) attempt to predict the initial population in a new environment by mining the relationships between solutions during historical environment changes. However, the complex relationships between solutions and the limited amount of available data often make it difficult to extract useful information efficiently, which may deteriorate the prediction accuracy. To address this problem, this article proposes a spatial–temporal topological tensor-based prediction method to generate the initial population in a new environment under the decomposition framework of MOEA/D. The method relies on the idea that the population distribution in each environment has topological similarity along the time dimension in the objective space, which makes it efficient to represent the population distribution in terms of a tensor and predict new solutions along each decomposition axis in a new environment by an improved tensor-based multishort time series prediction method. Experimental results on various benchmark problems and a real-world problem show that the proposed method is competitive or even superior to state-of-the-art dynamic multiobjective EAs based on prediction strategies. 1997-2012 IEEE.

关键词： Tensors

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Joint Entropy Enhanced Multi-objective Bayesian optimization for Graph Pruning

Joint Entropy Enhanced Multi-objective Bayesian Optimization...

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2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion

作者： Zhang, Weiwei Xu, Meiling Tang, Lixin National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Northeastern University Shenyang China

ISBN: (纸本)9798400704956

Graph learning processes entire attributed graphs, posing computational and scalability challenges. This study proposes a joint entropy enhanced multi-objective Bayesian optimization approach (JE-MoBopt) for efficient graph pruning. By employing the non-dominated sorting genetic algorithm II (NSGA-II), JE-MoBopt can effectively identify the Pareto front calculated by the surrogate models. Furthermore, we introduce a joint entropy search (JES) acquisition function to select an appropriate point from the Pareto solution set, thereby refining the surrogate models. We evaluate the JE-MoBopt approach on brain networks to satisfy both efficiency and accuracy requirements while preserving the important edges. © 2024 Copyright held by the owner/author(s).

关键词： Genetic algorithms

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Bi-objective approach for lot-sizing problem in cold rolling production planning

Bi-objective approach for lot-sizing problem in cold rolling...

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2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion

作者： Jia, Weiyan Tang, Lixin Yang, Yang Dong, Yun National Frontiers Science Center for Industrial Intelligence and Systems Optimization Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Ministry of Education Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Shenyang China Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Shenyang China

ISBN: (纸本)9798400704956

Planning in the cold rolling is an increasingly important vital aspect in steel industry. In this study, we investigate a cold rolling planning problem derived from a steel company, which is characterized by multi-item considerations with sequence-dependent setups in a single period. To tackle this problem, we introduce a bi-objective modified lot-sizing model, aiming at minimizing both the capacity waste and setup cost. A dedicated multi-objective evolutionary algorithm is proposed to solve this problem. The experiment results demonstrate the effectiveness of the proposed algorithm in achieving near-optimal solutions within a reasonable time, which can facilitate reductions in both production costs and time. © 2024 Copyright held by the owner/author(s).

关键词： evolutionary algorithm

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An Enhanced Surrogate-Assisted Multi-Objective Differential Evolution Algorithm with Self-Adaptive Strategies for Order Planning in Hot Rolling Process: Surrogate-Assisted MODE with Self-Adaptive Strategies for OPHR Problem

An Enhanced Surrogate-Assisted Multi-Objective Differential ...

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2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion

作者： Che, Gelegen Tang, Lixin Zhao, Guodong Guo, Qingxin Guo, Jianqiang National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Liaoning Shenyang China Northeastern University National Frontiers Science Center for Industrial Intelligence and Systems Optimization Liaoning Shenyang China Northeastern University Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Shenyang China

ISBN: (纸本)9798400704956

This paper studies a crucial decision-making problem of order planning for a hot rolling process (OPHR). The OPHR problem is to determine the optimal sequence of orders, with the goals of minimizing penalties for earliness/tardiness, maximizing profits from special orders, and minimizing transition penalties of adjacent orders. A multi-objective differential evolution (MODE) method with self-adaptive strategies (SaMODE) is developed to tackle it. Then, a radial basis function neural network is integrated into the SaMODE (RBFNN-SaMODE) as a surrogate model to reduce the computational burden of evaluating objectives throughout evolution. The proposed method is extensively tested on real production data, and the results demonstrate its superiority over the classical MODE, yielding highly effective solutions. © 2024 held by the owner/author(s).

关键词： Radial basis function networks

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A real-time temperature prediction method based on CNN-LSTM with MODE in steelmaking process

A real-time temperature prediction method based on CNN-LSTM ...

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IISE Annual Conference and Expo 2024

作者： Wang, Dan Liu, Chang Tang, Lixin Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Shenyang110819 China Ministry of Education Shenyang110819 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang110819 China

ISBN: (纸本)9781713877851

Prediction of molten steel quality is a key issue in the converter steelmaking process. Due to high temperature and physical-chemical reaction, it is difficult to predict the temperature of molten steel in real time through mechanism analysis. This is a challenging problem with black box characteristics. Here we propose a Convolutional neural networks (CNN) and long short-term memory (LSTM) networks model based on multi-objective differential evolution algorithm (MODE) for the real-time prediction problem in the steelmaking process. Specifically, the CNN is used to extract feature between multiple variables that affect the temperature of molten steel, and the LSTM is suitable for the time series model based on the irregular trends information. The multi-step output CNN-LSTM method is used to construct the temperature prediction model of molten steel, and a knee solution strategy is applied to MODE, which can balance the model accuracy and generalization ability. The experiments were conducted using real data collected from the converter steelmaking process. The results verify the effectiveness of the proposed model on multiple steel grades and outperform other artificial intelligence methods in multi-task prediction. © IISE Annual Conference and Expo *** rights reserved.

关键词： Convolutional neural networks

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Steel Defect Detection Algorithm Based on Multiobjective ENAS Sharing Strategy 24

Steel Defect Detection Algorithm Based on Multiobjective ENA...

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2nd International Conference on Frontiers of Intelligent Manufacturing and Automation, CFIMA 2024

作者： Zhao, Tianchen Dong, Zhiming Wang, Xianpeng National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China Ministry of Education Shenyang China Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Shenyang China Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Northeastern University Shenyang China

ISBN: (纸本)9798400710681

When employing the network architecture search approach for designing a steel surface defect detector, there are issues with conflicting evaluation metrics and limited computational resources. To address this challenge, we propose a multiobjective evolutionary sharing strategy-based network architecture search method (MESNAS). MESNAS defines network architecture search as a bi-level multiobjective optimization problem combined with a weight sharing acceleration strategy designed to quickly and simultaneously optimize the accuracy and complexity of classification models. A refined search space suitable for different image classification and detection tasks is designed. Using the MOEA/D algorithm, which incorporates a variable-length encoding scheme, the search space is co-utilized and explored through targeted improvements to the crossover and mutation operators in the offspring generation process. The optimal network architecture obtained from the search is tested on the concrete surface defect classification task, and the steel continuous casting slab surface defect detection task. The experimental results show that the proposed algorithm has good model performance and portability in image classification as well as defect detection tasks with different data complexities and different categories. © 2024 Copyright held by the owner/author(s).

关键词： Continuous casting

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A Multi-Objective Evolutionary Machine Learning Method for the Yield Strength Prediction of Strip Steel in Continuous Annealing

A Multi-Objective Evolutionary Machine Learning Method for t...

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IISE Annual Conference and Expo 2024

作者： Liu, Chang Tang, Lixin Zhang, Kainan Guo, Jiajing National Frontiers Science Center for Industrial Intelligence and Systems Optimization North-eastern University Shenyang110819 China Ministry of Education Shenyang110819 China Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Shenyang110819 China Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Shenyang110819 China

ISBN: (纸本)9781713877851

In the continuous annealing process, the yield strength is the key property index to evaluate the quality of strip steel and guide the production. It is difficult to perform online quality measurement for numerous products in the steel industry, this brings significant challenges for process monitoring and operation optimization. In this paper, a multiobjective evolutionary machine learning method is proposed for the yield strength prediction of strip steel in continuous annealing. First, the factors affecting the yield strength of strip steel are analyzed and the prediction model based on a hybrid kernel support vector regression (HKSVR) is established. To improve the prediction accuracy, a multi-objective evolutionary optimization algorithm with a knee solution strategy is used to optimize the key parameters of the HKSVR. It is verified by the actual production data that the proposed method has high prediction accuracy and can meet the actual production demand. Moreover, the proposed method can help practitioners to construct quality analytics models for many other similar production lines, providing assurance for quality control and optimization with a good generalization. © IISE Annual Conference and Expo *** rights reserved.

关键词： Prediction models

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BERD+: A Generic Sequential Recommendation Framework by Eliminating Unreliable Data with Item- and Attribute-level Signals

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ACM Transactions on Information systems 2023年第2期42卷 1-33页

作者： Sun, Yatong Yang, Xiaochun Sun, Zhu Wang, Bin School of Computer Science and Engineering Northeastern University School of Computing Macquarie University China Australia Institute of High Performance Computing Centre for Frontier AI Research A*STAR Singapore School of Computer Science and Engineering Northeastern University China National Frontiers Science Center for Industrial Intelligence and Systems Optimization China Key Laboratory of Data Analytics and Optimization for Smart Industry [Northeastern University Ministry of Education China

A preliminary work of our study was published at IJCAI’21 [47], which is substantially extended in the following aspects: (1) In Section 1, we analyze the necessity of introducing item attributes for detecting unreliable instances, together with the problems and challenges that attributes may bring in. (2) In Section 2, we add discussions about the limitations of existing attribute-aware recommender systems (Section 2.2) and denoising methods (Section 2.3) in the context of detecting unreliable instances. (3) In Section 4.2, we further conduct an in-depth analysis at the attribute level to demonstrate the capability of attributes for rectifying instance loss and uncertainty, as well as the disturbance caused by attributes. (4) We generalize BERD to a generic framework BERD+ in Section 5.1, equipped with novel modules, i.e., HU-GCN (Section 5.2) and EPE (Section 5.4), which properly incorporate item attributes while reducing their disturbance for rectifying instance uncer tainty (Section 5.5) and loss (Section 5.6). The generic BERD+ can be flexibly plugged into existing SRSs for performance enhanced recommendation via eliminating unreliable data. (5) In Section 6.2, we apply our BERD+ framework to seven state-of-the-art SRSs on five real-world datasets to illustrate its superiority. (6) To avoid unfair comparison caused by item attributes, we build and compare with the baseline that combines the original BERD and an advanced attribute-aware recommender system, KSR [19]. (7) For more comprehensive comparison, in Section 6.2.2, we compare BRED+ with two state-of-the-art denoising approaches;in Section 6.2.3, to examine the efficacy of HU-GCN and EPE, we compare HU-GCN with various attribute embedding techniques, i.e., variants of graph neural networks, and compare EPE with different attribute fusing methods, i.e., adding, concatenation, and weighted sum. (8) In Section 6.2.4, a detailed ablation study is conducted to verify the effectiveness of each module of BERD+. (

关键词： Convolution

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Learning discriminative foreground-and-background features for few-shot segmentation

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Multimedia Tools and Applications 2024年第18期83卷 55999-56019页

作者： Jiang, Cong Zhou, Yange Liu, Zhaoshuo Feng, Chaolu Li, Wei Yang, Jinzhu School of Computer Science and Engineering Northeastern University Liaoning Shenyang110819 China College of Science Northeastern University Liaoning Shenyang110819 China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Liaoning Shenyang110819 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Liaoning Shenyang110819 China

Few-shot Semantic Segmentation (FSS) endeavors to segment novel categories in a query image by referring to a support set comprising only a few annotated examples. Presently, many existing FSS methodologies primarily embrace the prototype learning paradigm and concentrate on optimizing the matching mechanism. However, these approaches tend to overlook the discrimination between the features of foreground background. Consequently, the segmentation results are often imprecise when it comes to capturing intricate structures, such as boundaries and small objects. In this study, we introduce the Discriminative Foreground-and-Background feature learning Network (DFBNet) to enhance the distinguishability of bilateral features. DFBNet comprises three major modules: a multi-level self-matching module (MSM), a feature separation module (FSM), and a semantic alignment module (SAM). The MSM generates prior masks separately for the foreground and background, employing a self-matching strategy across different feature levels. These prior masks are subsequently used as scaling factors within the FSM, where the features of the query’s foreground and background are independently scaled up and then concatenated along the channel dimension. Furthermore, we incorporate a two-layer Transformer encoder-based semantic alignment module (SAM) in DFBNet to refine the features, thereby creating a greater distinction between the foreground and background features. The performance of DFBNet is evaluated on the PASCAL-5i and COCO-20i benchmarks, demonstrating its superiority over existing solutions and establishing new state-of-the-art results in the field of few-shot semantic segmentation. The codes will be released if this paper is accepted. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Semantic Segmentation

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Immoral post detection using a one-dimensional convolutional neural network-based LSTM network

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Multimedia Tools and Applications 2025年 1-20页

作者： Saqia, Bibi Khan, Khairullah Rahman, Atta Ur Khan, Aurangzeb Jain, Deepak Kumar Department of Computer Science University of Science and Technology Bannu28100 Pakistan Riphah Institute of Systems Engineering Riphah International University Islamabad46000 Pakistan Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian116024 China Symbiosis Institute of Technology Symbiosis International University Pune India

In text mining and Natural Language Processing (NLP), extracting emotions from textual data is gaining rapid attraction. The proliferation of online content and the freedom of expression on social media platforms has compelled the advancement of more sophisticated methods for the automatic identification of unwanted content. The One-Dimensional Convolutional Neural Network (1D-CNN) performs best for feature extraction, while the Long Short-Term Memory Network (LSTM) is well-known for extracting sequential features and classification of textual data. This study proposes a novel solution by combining 1D-CNN with LSTM. Utilizing a context-aware model, this study aims to enhance the detection of immoral posts, including hate speech and other forms of immoral content on social media platforms. Various posts are extracted from social media, which are then processed using Glove word embedding models. It depicts the word representations in the text, which yields the form of a real-valued vector determining the words' meaning. The 1D convolution utilizes a filter window over the input time-series data to extract features. Relying on the acquired parameters of the filters, they act as feature extractors. This feature extraction technique allows the model to distinguish detailed patterns and nuances from the textual contents of posts. The sequential processing capabilities of the LSTM network enable an in-depth examination of the text, capturing temporal relationships and context, which is critical for successful content categorization. To validate the proposed model, we utilized a benchmark dataset, called the Twitter dataset. The proposed model achieved the highest accuracy of 95.37% and outperforms state-of-the-art works. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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