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Distributed asynchronous double accelerated optimization for ethylene plant considering delays

Chinese Journal of Chemical Engineering 2025年第02期 245-250页

作者： Ting Wang Zhongmei Li Wenli Du State Key Laboratory of Industrial Control Technology East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology

Considering the complexity of plant-wide optimization for large-scale industries, a distributed optimization framework to solve the profit optimization problem in ethylene whole process is proposed. To tackle the delays arising from the residence time for materials passing through production units during the process with guaranteed constraint satisfaction, an asynchronous distributed parameter projection algorithm with gradient tracking method is introduced. Besides, the heavy ball momentum and Nesterov momentum are incorporated into the proposed algorithm in order to achieve double acceleration properties. The experimental results show that the proposed asynchronous algorithm can achieve a faster convergence compared with the synchronous algorithm.

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Cosmological perturbations in the energy-momentum squared gravity theory:constraints from gravitational wave standard sirens and redshift space distortions

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Chinese Physics C 2025年第4期49卷 199-208页

作者： Qi-Ming Fu Xin Zhang Institute of Physics Shaanxi University of TechnologyHanzhong 723000China Lanzhou Center for Theoretical Physics Key Laboratory of Theoretical Physics of Gansu Provinceand Key Laboratory of Quantum Theory and Applications of MoELanzhou UniversityLanzhou 730000China Key Laboratory of Cosmology and Astrophysics(Liaoning)&College of Sciences Northeastern UniversityShenyang 110819China Key Laboratory of Data Analytics and Optimization for Smart Industry(Ministry of Education) Northeastern UniversityShenyang 110819China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern UniversityShenyang 110819China

We investigate the linear cosmological perturbations in the context of the so-called energy-momentum squared gravity(EMSG)*** research shows that the EMSG theory can reproduce a viable background cosmological evolution comparable toΛCDM,whereas the matter-dominated era exhibits slight *** this paper,we focus on power-law EMSG models and derive the equations for the linear cosmological *** explore the propagation of the gravitational wave(GW)and the growth of matter density perturbation at the first order,and we estimate the model parameters from the simulated GW and observed redshift space distortion *** analysis reveals that the model parameters should be small and positive in the confidence interval,which indicates that the theory agrees closely with the observational data and can be considered an alternative to the standard cosmological model.

关键词： energy-momentum squared gravity cosmological perturbations gravitational wave cosmological parameters

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3DGCformer: 3-Dimensional Graph Convolutional transformer for multi-step origin–destination matrix forecasting

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Applied Intelligence 2025年第6期55卷 1-18页

作者： Huang, Yiou Deng, Hao Zhao, Shengjie School of Computer Science and Technology Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Shanghai China Engineering Research Center of Key Software Technologies for Smart City Perception and Planning Ministry of Education Shanghai China

Forecasting Human mobility is of great significance in the simulation and control of infectious diseases like COVID-19. To get a clear picture of potential future outbreaks, it is necessary to forecast multi-step Origin–Destination (OD) matrices for a relatively long period in the future. However, multi-step Origin–Destination Matrix Forecasting (ODMF) is a non-trivial problem. First, previous ODMF models only forecast the OD matrix for the next time-step, and they cannot perform well on long-term multi-step forecasts due to error accumulation. Second, many ODMF methods capture spatial and temporal dependencies with separate modules, which is insufficient to model spatio-temporal correlations in the time-varying OD matrix sequence. To address the challenges in multi-step ODMF, we propose 3-Dimensional Graph Convolutional Transformer (3DGCformer). As an enhancement of the original 3DGCN, we propose a novel Origin–Destination Feature Propagation (ODFP) rule between 3DGCN layers and integrate 2 3DGCNs with different spatio-temporal graphs and corresponding feature propagation rules to model the formation of OD flows in a more comprehensive way. For multi-step forecasts, 3DGCformer uses Transformer to capture long-term global temporal dependency, and adapt its decoder using labeled tokens to avoid error accumulation and improve time efficiency. To avoid information loss as the number of regions increases, we propose a patch embedding approach to convert data from 3DGCNs to the Transformer module. We perform extensive experiments on 4 real-world human mobility datasets, and the results show that our proposed model outperforms the state-of-the-art methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolution

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A surrogate archive assisted multi-objective evolutionary algorithm under limited computational budget

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Soft Computing 2025年第2期29卷 723-753页

作者： Wang, Le Fan, Qinqin Yan, Xuefeng Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai200237 China Logistics Research Center Shanghai Maritime University Shanghai201306 China

The concept of archives has received significant attention within the realm of multi-objective optimization. However, there exists a substantial gap in exploring archives within surrogate-assisted multi-objective evolutionary algorithms (SAMOEAs). In this paper, a novel framework called surrogate archive assisted multi-objective evolutionary algorithm (SAAMOEA) is introduced. The framework uses a surrogate archive to guide the evolution of the algorithm. This archive serves as an external database to store individuals that perform better during evolution, reducing the randomness of the individuals. Firstly, the surrogate archive is initialized using multiple MOEAs, and the surrogate constructed by the individuals in this initialization archive are used to approximate the exact function. Secondly, a cluster-based model management strategy is proposed that employs K-means method to fit the Pareto front and identify infilled individuals. Simultaneously, an environment selection method is proposed. The method determines whether infilled individuals are used to update the surrogate by calculating a convergence quality metric. The quality of the individuals in the surrogate archive is maintained inversely through the two strategies described above to provide conditions for accurate model construction. Furthermore, the paper presents three hypotheses about the relationship between the Pareto front of the surrogate model and the Pareto front of the exact function evaluation. The SAAMOEA was compared to ten state-of-the-art (SOTA) algorithms, and simulation experiments were conducted on 12 benchmark functions to obtain non-parametric estimation results and distribution plots of the true PF. Additionally, SAAMOEA was validated on an airfoil design optimization problem. The HV results and optimized parameter results demonstrate the performance advantages of the algorithm. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Budget control

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Hybrid model of multimodal based on data enhancement and lumped reaction kinetics: Applying to industrial ebullated-bed residue hydrogenation unit

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Chinese Journal of Chemical Engineering 2025年第02期 284-302页

作者： Jian Long Mengru Zhang Anlan Li Cheng Huang Dong Xue Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Sinopec Engineering Incorporation

Industrial ebullated-bed is an important device for promoting the cleaning and upgrading of oil *** lumped kinetic model is a powerful tool for predicting the product yield of the ebullated-bed residue hydrogenation(EBRH) unit, However, during the long-term operation of the device, there are phenomena such as low frequency of material property analysis leading to limited operating data and diverse operating modes at the same time scale, which poses a huge challenge to building an accurate product yield prediction model. To address these challenges, a data augmentation-based eleven lumped reaction kinetics mechanism model was constructed. This model combines generative adversarial networks, outlier elimination, and L2norm data filtering to expand the dataset and utilizes kernel principal component analysis-fuzzy C-means for operating condition partitioning. Based on the hydrogenation reaction mechanism, a single and sub operating condition eleven lumped reaction kinetics model of an ebullated-bed residue hydrogenation unit, comprising 55 reaction paths and 110 parameters, was constructed before and after data augmentation. Compared to the single model before data enhancement,the average absolute error of the sub-models under data enhancement division was reduced by 23%.Thus, these findings can help guide the operation and optimization of the production process.

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Byzantine-robust distributed support vector machine

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science China Mathematics 2025年第3期68卷 707-728页

作者： Xiaozhou Wang Weidong Liu Xiaojun Mao School of Statistics East China Normal UniversityShanghai 200062China Key Laboratory of Advanced Theory and Application in Statistics and Data Science(Ministry of Education) East China Normal UniversityShanghai 200062China School of Mathematical Sciences Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of Artificial Intelligence(Ministry of Education) Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of Scientific and Engineering Computing(Ministry of Education) Shanghai Jiao Tong UniversityShanghai 200240China

The development of information technology brings diversification of data sources and large-scale data sets and calls for the exploration of distributed learning algorithms. In distributed systems, some local machines may behave abnormally and send arbitrary information to the central machine(known as Byzantine failures), which can invalidate the distributed algorithms based on the assumption of faultless systems. This paper studies Byzantine-robust distributed algorithms for support vector machines(SVMs) in the context of binary classification. Despite a vast literature on Byzantine problems, much less is known about the theoretical properties of Byzantine-robust SVMs due to their unique challenges. In this paper, we propose two distributed gradient descent algorithms for SVMs. The median and trimmed mean operations in aggregation can effectively defend against Byzantine failures. Theoretically, we show the convergence of the proposed estimators and provide the statistical error rates. After a certain number of iterations, our estimators achieve near-optimal rates. Simulation studies and real data analysis are conducted to demonstrate the performance of the proposed Byzantine-robust distributed algorithms.

关键词： Byzantine robustness convergence distributed learning support vector machine

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An Evolutionary Multitasking Algorithm for Efficient Multiobjective Recommendations

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 518-532页

作者： Tian, Ye Ji, Luke Hu, Yiwei Ma, Haiping Wu, Le Zhang, Xingyi Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Hefei230601 China Anhui University Institutes of Physical Science and Information Technology Hefei230601 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Hefei230029 China

Represented by evolutionary algorithms and swarm intelligence algorithms, nature-inspired metaheuristics have been successfully applied to recommender systems and amply demonstrated effectiveness, in particular, for multiobjective recommendation. Owing to the population-based search paradigm, these algorithms can produce a number of recommendation lists, making diverse tradeoffs between multiple metrics and meeting the requirements of accuracy, novelty, diversity, and other user preferences. However, these algorithms are criticized for the low efficiency of the optimization process, especially when the number of users is large. To address this issue, this article proposes an evolutionary multitasking-based recommendation method, where each task corresponds to a user and all the tasks are optimized simultaneously, thus highly improving the efficiency of recommendation. To enhance the convergence speed, all the users are divided into multiple populations according to the similarity between their preferences, where each population evolves with internal knowledge transfer between users, and all the populations evolve with external knowledge transfer between populations. Experimental results on various datasets verify that the proposed method can better balance between multiple metrics than classical and deep neural network-based recommendation methods and exhibits significantly higher efficiency than evolutionary multiobjective optimization-based recommendation methods. © 2024 IEEE. All rights reserved.

关键词： Multiobjective optimization

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Adaptive Intention Learning for Session-Based Recommendation

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ACM Transactions on Intelligent Systems and Technology 2025年第2期16卷 1-26页

作者： Zhang, Qingbo Yang, Xiaochun Chen, Hao Wang, Bin Sun, Zhu Zhou, Xiangmin Computer Science and Engineering Northeastern University Shenyang China Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang China Singapore University of Technology and Design Singapore Singapore Rmit University Melbourne Australia Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Ministry of Education Shenyang China

In recent years, session-based recommender systems (SRSs) have emerged as a significant research focus within the recommendation field. Capturing user intentions to infer user interest accordingly has proven to be effective in enhancing the accuracy of SRSs. However, existing techniques assume that all sessions have the same number of intentions or that the items in one category belonging to the same session reflect the same intention. In real applications, such as e-commerce, sessions may have different numbers of intentions, and the same type of items in a session may correspond to different intentions. As a result, existing techniques cannot guarantee high-quality user interest prediction. In this article, we propose a novel Adaptive Intention Learning Network (AILN) to capture an adaptive number of intentions for each session, thereby enhancing the accuracy of user interest inference. Specifically, we design an intention evaluation network (IEN) to evaluate whether a subsequence of a session corresponds to a valid intention, and an intention generation network (IGN) to learn the representation of a valid intention. By checking each subsequence of a session, IEN and IGN enable the incremental learning of a session-specific intention hierarchy (IH) to store valid intentions of the session. To reduce the cost of building the IH, we propose a pruning strategy that exploits the intention validity to avoid unnecessary evaluation. The representative intentions are selected from IH and input into a designed interest predictor to infer the user interest. Experimental results on two real-world datasets demonstrate the superiority of our proposed AILN. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Recommender systems

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An improved biogeography-based optimization algorithm for fuzzy distributed flexible job-shop scheduling problem

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Kongzhi Lilun Yu Yingyong/Control Theory and Applications 2025年第4期42卷 713-721页

作者： Sun, Mei-Ling Gu, Xing-Sheng Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai200237 China

The globalization of the economy has prompted manufacturing enterprises to transition from a single factory to a multi-factory collaborative model, making the fuzzy distributed flexible job-shop scheduling problem (FDFJSP) a research hotspot in the scheduling field. In this paper, a novel biogeography-based optimization algorithm based on simulated annealing and local search strategy (BBOSL) is proposed to minimize the maximum fuzzy completion time of FDFJSP. Based on the characteristics of the problem, a new factory-random key encoding and decoding scheme is designed. Scheduling rules are used to generate half of the initial population to improve the population quality. A new solution acceptance method based on a simulated annealing algorithm and a local search strategy based on a critical factory are proposed to enhance the search capability. The algorithm parameters are tuned to improve algorithm performance. The experimental results validate the effectiveness of the improved strategy and compare it with the existing algorithms to verify its superiority in fuzzy centralized and fuzzy distributed flexible job-shop scheduling problems. © 2025 South China University of Technology. All rights reserved.

关键词： Simulated annealing

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Development of a high-resolution near-surface meteorological forcing dataset for the Third Pole region

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science China Earth sciences 2025年第4期68卷 1274-1290页

作者： Yaozhi JIANG Wenjun TANG Kun YANG Jie HE Changkun SHAO Xu ZHOU Hui LU Yingying CHEN Xin LI Jiancheng SHI School of Resources and Environment University of Electronic Science and Technology of ChinaChengdu611731China Department of Earth System Science Ministry of Education Key Laboratory for Earth System ModelingInstitute for Global Change StudiesTsinghua UniversityBeijing100084China National Tibetan Plateau Data Center State Key Laboratory of Tibetan Plateau Earth SystemEnvironment and ResourcesInstitute of Tibetan Plateau ResearchChinese Academy of SciencesBeijing100101China State Key Laboratory of Hydroscience and Engineering Tsinghua UniversityBeijing100084China Tsinghua University(Department of Earth System Science)-Xi’an Institute of Surveying and Mapping Joint Research Center for Next-Generation Smart Mapping Beijing100084China National Space Science Center Chinese Academy of SciencesBeijing100190China

Recently,the Third Pole(TP)region has experienced rapid environmental *** data are essential for hydrometeorological and ecological applications but still have large uncertainties on the TP owing to the heterogeneous land surface,complex terrain,and sparse weather *** this study,a long-term(1979–2020)high-resolution(1/30°)meteorological forcing dataset for the TP(TPMFD)was developed,as a sister to the widely used China Meteorological Forcing dataset(CMFD).The TPMFD comprises seven components necessary for driving land surface *** have previously contributed precipitation and downward shortwave radiation data for the TPMFD,and this study presents the development of five other components and focuses on validations for all ***,2-meter air temperature,2-meter specific humidity,10-meter wind speed,and surface air pressure were generated by combining the fifth-generation atmospheric reanalysis for European Center for Medium-Range Weather Forecasts(ERA5),a short-term high-resolution atmospheric simulation,and in situ observations,and the downward longwave radiation was calculated using semi-physical *** cross-validation and independent-validation demonstrated that most variables in the developed dataset outperformed those in widely used reanalysis datasets,including ERA5,ERA5-Land,and the Global Land data Assimilation System(GLDAS).This dataset is expected to be beneficial for climate analyses and modeling applications of land-surface processes on the TP.

关键词： Meteorological data Third Pole High-resolution data downscaling and merging

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