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

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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Reinforcement Learning-Based Dynamic Order Recommendation for On-Demand Food Delivery

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Tsinghua Science and Technology 2024年第2期29卷 356-367页

作者： Xing wang ling wang Chenxin Dong Hao Ren Ke Xing Department of Automation Tsinghua UniversityBeijing 100080China School of Mechanical and Automotive Engineering Qingdao Hengxing University of Science and TechnologyQingdao 266100China Meituan Beijing 100015China

On-demand food delivery(OFD)is gaining more and more popularity in modern *** a kernel order assignment manner in OFD scenario,order recommendation directly influences the delivery efficiency of the platform and the delivery experience of *** paper addresses the dynamism of the order recommendation problem and proposes a reinforcement learning solution *** actor-critic network based on long short term memory(LSTM)unit is designed to deal with the order-grabbing conflict between different ***,three rider sequencing rules are accordingly proposed to match different time steps of the LSTM unit with different *** test the performance of the proposed method,extensive experiments are conducted based on real data from Meituan delivery *** results demonstrate that the proposed reinforcement learning based order recommendation method can significantly increase the number of grabbed orders and reduce the number of order-grabbing conflicts,resulting in better delivery efficiency and experience for the platform and riders.

关键词： on-demand food delivery order recommendation reinforcement learning actor-critic network long short term memory

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A Two-Layer Encoding Learning Swarm Optimizer Based on Frequent Itemsets for Sparse Large-Scale Multi-Objective Optimization

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1342-1357页

作者： Sheng Qi Rui wang Tao Zhang Xu Yang Ruiqing Sun ling wang College of Systems Engineering National University of Defense TechnologyChangsha 410073China Xiangjiang Laboratory Changsha 410205China College of Computer National University of Defense TechnologyChangsha 410073China Department of Automation Tsinghua UniversityBeijing 100084China IEEE

Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are *** a result,many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec ***,existing optimizers often focus on locating non-zero variable posi-tions to optimize the binary variables ***,approxi-mating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is *** data mining,it is common to mine frequent itemsets appear-ing together in a dataset to reveal the correlation between *** by this,we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets(TELSO)to address these *** mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast ***-mental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.

关键词： Evolutionary algorithms learning swarm optimiza-tion sparse large-scale optimization sparse large-scale multi-objec-tive problems two-layer encoding.

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Enhancing Evolutionary Algorithms With Pattern Mining for Sparse Large-Scale Multi-Objective Optimization Problems

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IEEE/CAA Journal of Automatica Sinica 2024年第8期11卷 1786-1801页

作者： Sheng Qi Rui wang Tao Zhang Weixiong Huang Fan Yu ling wang College of Systems Engineering National University of Defense TechnologyChangsha 410073China College of Systems Engineering National University of Defense TechnologyChangsha 410073and also with the Xiangjiang LaboratoryChangsha 410205China College of Transportation and Engineering Central South UniversityChangsha 410073China Department of Automation Tsinghua UniversityBeijing 100084China

Sparse large-scale multi-objective optimization problems(SLMOPs)are common in science and ***,the large-scale problem represents the high dimensionality of the decision space,requiring algorithms to traverse vast expanse with limited computational ***,in the context of sparse,most variables in Pareto optimal solutions are zero,making it difficult for algorithms to identify non-zero variables *** paper is dedicated to addressing the challenges posed by *** start,we introduce innovative objective functions customized to mine maximum and minimum candidate *** substantial enhancement dramatically improves the efficacy of frequent pattern *** this way,selecting candidate sets is no longer based on the quantity of nonzero variables they contain but on a higher proportion of nonzero variables within specific ***,we unveil a novel approach to association rule mining,which delves into the intricate relationships between non-zero *** novel methodology aids in identifying sparse distributions that can potentially expedite reductions in the objective function *** extensively tested our algorithm across eight benchmark problems and four real-world *** results demonstrate that our approach achieves competitive solutions across various challenges.

关键词： Evolutionary algorithms pattern mining sparse large-scale multi-objective problems(SLMOPs) sparse large-scale optimization.

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Learning to Branch in Combinatorial Optimization With Graph Pointer Networks

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 157-169页

作者： Rui wang Zhiming Zhou Kaiwen Li Tao Zhang ling wang Xin Xu Xiangke Liao IEEE the Xiangjiang Laboratory and the College of Systems Engineering National University of Defense TechnologyChangsha 410073China the Institute of Automation Chinese Academy of SciencesBeijing 100190China the College of Systems Engineering National University of Defense TechnologyChangsha 410073 the Hunan Key Laboratory of Multi-Energy System Intelligent Interconnection Technology Changsha 410073China the Department of Automation Tsinghua UniversityBeijing 100084China the College of Intelligence Science and Technology National University of Defense TechnologyChangsha 410073China the College of Computer Science and Technology National University of Defense TechnologyChangsha 410073China

Traditional expert-designed branching rules in branch-and-bound(B&B) are static, often failing to adapt to diverse and evolving problem instances. Crafting these rules is labor-intensive, and may not scale well with complex *** the frequent need to solve varied combinatorial optimization problems, leveraging statistical learning to auto-tune B&B algorithms for specific problem classes becomes attractive. This paper proposes a graph pointer network model to learn the branch rules. Graph features, global features and historical features are designated to represent the solver state. The graph neural network processes graph features, while the pointer mechanism assimilates the global and historical features to finally determine the variable on which to branch. The model is trained to imitate the expert strong branching rule by a tailored top-k Kullback-Leibler divergence loss function. Experiments on a series of benchmark problems demonstrate that the proposed approach significantly outperforms the widely used expert-designed branching rules. It also outperforms state-of-the-art machine-learning-based branch-and-bound methods in terms of solving speed and search tree size on all the test instances. In addition, the model can generalize to unseen instances and scale to larger instances.

关键词： Branch-and-bound(B&B) combinatorial optimization deep learning graph neural network imitation learning

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Significant risk factors for intensive care unit-acquired weakness:A processing strategy based on repeated machine learning

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World Journal of Clinical Cases 2024年第7期12卷 1235-1242页

作者： ling wang Deng-Yan Long Intensive Care Unit People's Hospital of Qiandongnan Miao and Dong Autonomous PrefectureKaili 556000Guizhou ProvinceChina

BACKGROUND Intensive care unit-acquired weakness(ICU-AW)is a common complication that significantly impacts the patient's recovery process,even leading to adverse ***,there is a lack of effective preventive *** To identify significant risk factors for ICU-AW through iterative machine learning techniques and offer recommendations for its prevention and *** Patients were categorized into ICU-AW and non-ICU-AW groups on the 14th day post-ICU *** data from the initial 14 d of ICU stay,such as age,comorbidities,sedative dosage,vasopressor dosage,duration of mechanical ventilation,length of ICU stay,and rehabilitation therapy,were *** relationships between these variables and ICU-AW were *** iterative machine learning techniques,a multilayer perceptron neural network model was developed,and its predictive performance for ICU-AW was assessed using the receiver operating characteristic *** Within the ICU-AW group,age,duration of mechanical ventilation,lorazepam dosage,adrenaline dosage,and length of ICU stay were significantly higher than in the non-ICU-AW ***,sepsis,multiple organ dysfunction syndrome,hypoalbuminemia,acute heart failure,respiratory failure,acute kidney injury,anemia,stress-related gastrointestinal bleeding,shock,hypertension,coronary artery disease,malignant tumors,and rehabilitation therapy ratios were significantly higher in the ICU-AW group,demonstrating statistical *** most influential factors contributing to ICU-AW were identified as the length of ICU stay(100.0%)and the duration of mechanical ventilation(54.9%).The neural network model predicted ICU-AW with an area under the curve of 0.941,sensitivity of 92.2%,and specificity of 82.7%.CONCLUSION The main factors influencing ICU-AW are the length of ICU stay and the duration of mechanical ventilation.A primary preventive strategy,when feasible,involves minimizing both ICU stay and mechani

关键词： Intensive care unit-acquired weakness Risk factors Machine learning Prevention Strategies

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Promoting effect of FeOx addition on the mechanochemically prepared vanadium-based catalyst for real PCDD/Fs removal and mechanism insight

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Journal of Environmental Sciences 2024年第3期137卷 478-487页

作者： Minghui Tang ling wang Hongxian Li Xinlei Huang Cuicui Du Shengyong Lu State Key Laboratory for Clean Energy Utilization Institute for Thermal Power EngineeringZhejiang UniversityHangzhou 310027China

Industrial-use VOx-based catalysts usually have a higher active temperature window (>250-300℃),which becomes a“bottleneck”for the practical application of PCDD/Fs catalytic degradation *** this work,VO_(x)-FeO_(x)/TiO_(2) catalyst prepared via mechanochemically method was investigated for the catalytic removal of PCDD/*** removal efficiency of 1,2-DCBz,pure PCDD/Fs gas generated in the lab,PCDD/Fs from actual fue gas,long-term were studied,and the degradation mechanism was explored using FTIR and *** degradation efficiency of 1,2-DCBz and PCDD/Fs on VO_(x)-FeO_(x)/TiO_(2) were higher than that of VO_(x)/TiO_(2)catalyst,and the optimal FeOx addition ratio was 3 wt.%.The characterization results show that the addition of FeOx can effectively improve the pore structure,surface acidity,and VOx dispersion of the catalyst,thus contributing to increasing the V^(5+)content and surface-active oxygen,which is conducive to the improvement of adsorption and redox performance of the *** the actual MSWI (municipal solid waste incineration)fue gas,the PCDD/Fs removal efficiency over VTi-3Fe-MC maintained long-term stability,higher than 85%for 240 *** result was not significantly reduced compared with the data obtained in the *** to the analysis results of intermediate products by FTIR and GC-TOFMS,it can be inferred that the epoxidation fracture of benzene ring is the rate-limiting step of dioxin catalytic degradation *** work gives an in-depth view into the PCDD/Fs removal over VO_(x)-FeO_(x)/TiO_(2) catalysts and could provide guidelines for the rational design of reliable catalysts for industrial applications.

关键词： PCDD/F removal MSWI fue gas VO_(x)-FeO_(x)/TiO_(2) Mechanochemical GC-TOFMS

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A Matching Algorithm with Reinforcement Learning and Decoupling Strategy for Order Dispatching in On-Demand Food Delivery

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Tsinghua Science and Technology 2024年第2期29卷 386-399页

作者： Jingfang Chen ling wang Zixiao Pan Yuting Wu Jie Zheng Xuetao Ding Department of Automation Tsinghua UniversityBeijing 100084China Department of Delivery Technology MeituanBeijing 100102China

The on-demand food delivery(OFD)service has gained rapid development in the past decades but meanwhile encounters challenges for further improving operation *** order dispatching problem is one of the most concerning issues for the OFD platforms,which refer to dynamically dispatching a large number of orders to riders reasonably in very limited decision *** solve such a challenging combinatorial optimization problem,an effective matching algorithm is proposed by fusing the reinforcement learning technique and the optimization ***,to deal with the large-scale complexity,a decoupling method is designed by reducing the matching space between new orders and ***,to overcome the high dynamism and satisfy the stringent requirements on decision time,a reinforcement learning based dispatching heuristic is *** be specific,a sequence-to-sequence neural network is constructed based on the problem characteristic to generate an order priority ***,a training approach is specially designed to improve learning ***,a greedy heuristic is employed to effectively dispatch new orders according to the order priority *** real-world datasets,numerical experiments are conducted to validate the effectiveness of the proposed *** results show that the proposed algorithm can effectively solve the problem by improving delivery efficiency and maintaining customer satisfaction.

关键词： order dispatching on-demand delivery reinforcement learning decoupling strategy sequence-to-sequence neural network

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Stabilizing zinc anode using zeolite imidazole framework functionalized separator for durable aqueous zinc-ion batteries

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Journal of Energy Chemistry 2024年第3期90卷 23-31,I0003页

作者： Weisong Zhang Xinyan Zhu ling Kang Ziyu Peng Jing Zhu Liang Pan Lei Dai Shude Liu ling wang Yongguang Liu Zhangxing He School of Chemical Engineering North China University of Science and TechnologyTangshan 063009HebeiChina Shanghai Key Laboratory of Multidimensional Information Processing East China Normal UniversityShanghai 201620China College of Textiles Donghua UniversityShanghai 201620China

Aqueous zinc-ion batteries(AZIBs) hold great promise as a viable alternative to lithium-ion batteries owing to their high energy density and environmental ***,AZIBs are consistently plagued by the formation of zinc dendrites and concurrent side reactions,which significantly diminish their overall service life,In this study,the glass fiber separator(GF) is modified using zeolite imidazole salt framework-8(ZIF-8),enabling the development of efficient ***-8,which is abundant in nitrogen content,efficiently regulates the desolvation of [Zn(H_(2)O)_(6)]^(2+) to inhibit hydrogen ***,it possesses abundant nanochannels that facilitate the uniform deposition of Zn~(2+) via a localized action,thereby hindering the formation of *** insulating properties of ZIF-8 help prevent Zn^(2+) and water from trapping electron reduction at the layer surface,which reduces corrosion of the zinc ***,ZIF-8-GF achieves the even transport of Zn^(2+) and regulates the homogeneous deposition along the Zn(002) crystal surface,thus significantly enhancing the electrochemical performance of the AZIBs,In particular,the Zn|Zn symmetric cell with the ZIF-8-GF separator delivers a stable cycle life at0.5 mA cm^(-2) of 2300 *** Zn|ZIF-8-GF|MnO_(2) cell exhibits reduced voltage polarization while maintaining a capacity retention rate(93.4%) after 1200 cycles at 1.2 A g^(-1) The unique design of the modified diaphragm provides a new approach to realizing high-performance AZIBs.

关键词： Aqueous zinc-ion batteries Separators modifications ZIF-8 Zn deposition Dendrite-free

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Roles of transforming growth factor-βsignaling in liver disease

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World Journal of Hepatology 2024年第7期16卷 973-979页

作者： Xiao-ling wang Meng Yang Ying wang Clinical Laboratory Shanxi Academy of Traditional Chinese MedicineTaiyuan 030012Shanxi ProvinceChina

In this editorial we expand the discussion on the article by Zhang et al published in the recent issue of the World Journal of *** focus on the diagnostic and therapeutic targets identified on the basis of the current understanding of the molecular mechanisms of liver *** growth factor-β(TGF-β)belongs to a structurally related cytokine super *** family members display different time-and tissue-specific expression patterns associated with autoimmunity,inflammation,fibrosis,and tumorigenesis;and,they participate in the pathogenesis of many ***-βand its related signaling pathways have been shown to participate in the progression of liver diseases,such as injury,inflammation,fibrosis,cirrhosis,and *** often studied TGF-β/Smad signaling pathway has been shown to promote or inhibit liver fibrosis under different ***,the early immature TGF-βmolecule functions as a tumor suppressor,inducing apoptosis;but,its interaction with the mitogenic molecule epidermal growth factor alters this effect,activating anti-apoptotic signals that promote liver cancer ***,TGF-βsignaling displays contradictory effects in different liver disease ***,the use of TGF-βand related signaling pathway molecules for diagnosis and treatment of liver diseases remains a challenge and needs further *** this editorial,we aim to review the evidence for the use of TGF-βsignaling pathway molecules as diagnostic or therapeutic targets for different liver disease stages.

关键词： Transforming growth factor-βsignaling Liver disease Molecular mechanism Targets Diagnosis

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