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

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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 properties.First, 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

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 attention.Various constrained multi-objective optimization evolutionary algorithms(CMOEAs)have been developed with the use of different algorithmic strategies,evolutionary operators,and constraint-handling techniques.The performance of CMOEAs may be heavily dependent on the operators used,however,it is usually difficult to select suitable operators for the problem at hand.Hence,improving operator selection is promising and necessary for CMOEAs.This work proposes an online operator selection framework assisted by Deep Reinforcement Learning.The 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 reward.By 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 performance.The framework is embedded into four popular CMOEAs and assessed on 42 benchmark problems.The 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

Competitive and Cooperative-Based Strength Pareto Evolutionary Algorithm for Green Distributed Heterogeneous Flow Shop Scheduling

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Intelligent Automation & Soft Computing 2023年第8期37卷 2077-2101页

作者： Kuihua Huang Rui Li wenyin gong Weiwei Bian Rui Wang College of System Engineering National University of Defense TechnologyChangsha410073China School of Computer Science China University of GeosciencesWuhan430074China Equipment General Technology Laboratory Beijing Mechanical Equipment Research InstituteBeijing100854China

This work aims to resolve the distributed heterogeneous permutation flow shop scheduling problem(DHPFSP)with minimizing makespan and total energy consumption(TEC).To solve this NP-hard problem,this work proposed a competitive and cooperative-based strength Pareto evolutionary algorithm(CCSPEA)which contains the following features:1)An initialization based on three heuristic rules is developed to generate a population with great diversity and convergence.2)A comprehensive metric combining convergence and diversity metrics is used to better represent the heuristic information of a solution.3)A competitive selection is designed which divides the population into a winner and a loser swarms based on the comprehensive metric.4)A cooperative evolutionary schema is proposed for winner and loser swarms to accelerate the convergence of global search.5)Five local search strategies based on problem knowledge are designed to improve convergence.6)Aproblem-based energy-saving strategy is presented to reduce TEC.Finally,to evaluate the performance of CCSPEA,it is compared to four state-of-art and run on 22 instances based on the Taillard benchmark.The numerical experiment results demonstrate that 1)the proposed comprehensive metric can efficiently represent the heuristic information of each solution to help the later step divide the population.2)The global search based on the competitive and cooperative schema can accelerate loser solutions convergence and further improve the winner’s exploration.3)The problembased initialization,local search,and energy-saving strategies can efficiently reduce the makespan and TEC.4)The proposed CCSPEA is superior to the state-of-art for solving DHPFSP.

关键词： Distributed heterogeneous flow shop scheduling green scheduling SPEA2 competitive and cooperative

Load Optimization Scheduling of Chip Mounter Based on Hybrid Adaptive Optimization

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Complex System Modeling and Simulation 2023年第1期3卷 1-11页

作者： Xuesong Yan Hao Zuo Chengyu Hu wenyin gong Victor S.Sheng School of Computer Science China University of GeosciencesWuhan 430074China Department of Computer Science Texas Tech UniversityLubbockTX 79409−3104USA

A chip mounter is the core equipment in the production line of the surface-mount technology,which is responsible for finishing the mount operation.It is the most complex and time-consuming stage in the production process.Therefore,it is of great significance to optimize the load balance and mounting efficiency of the chip mounter and improve the mounting efficiency of the production line.In this study,according to the specific type of chip mounter in the actual production line of a company,a maximum and minimum model is established to minimize the maximum cycle time of the chip mounter in the production line.The production efficiency of the production line can be improved by optimizing the workload scheduling of each chip mounter.On this basis,a hybrid adaptive optimization algorithm is proposed to solve the load scheduling problem of the mounter.The hybrid algorithm is a hybrid of an adaptive genetic algorithm and the improved ant colony algorithm.It combines the advantages of the two algorithms and improves their global search ability and convergence speed.The experimental results show that the proposed hybrid optimization algorithm has a good optimization effect and convergence in the load scheduling problem of chip mounters.

关键词： Surface Mount Technology(SMT) chip mounter load optimization scheduling adaptive genetic algorithm ant colony algorithm

Sparse Reconstructive Evidential Clustering for Multi-View Data

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 459-473页

作者： Chaoyu gong Yang You School of Computing National University of SingaporeSingapore

Although many multi-view clustering(MVC) algorithms with acceptable performances have been presented, to the best of our knowledge, nearly all of them need to be fed with the correct number of clusters. In addition, these existing algorithms create only the hard and fuzzy partitions for multi-view objects,which are often located in highly-overlapping areas of multi-view feature space. The adoption of hard and fuzzy partition ignores the ambiguity and uncertainty in the assignment of objects, likely leading to performance degradation. To address these issues, we propose a novel sparse reconstructive multi-view evidential clustering algorithm(SRMVEC). Based on a sparse reconstructive procedure, SRMVEC learns a shared affinity matrix across views, and maps multi-view objects to a 2-dimensional humanreadable chart by calculating 2 newly defined mathematical metrics for each object. From this chart, users can detect the number of clusters and select several objects existing in the dataset as cluster centers. Then, SRMVEC derives a credal partition under the framework of evidence theory, improving the fault tolerance of clustering. Ablation studies show the benefits of adopting the sparse reconstructive procedure and evidence theory. Besides,SRMVEC delivers effectiveness on benchmark datasets by outperforming some state-of-the-art methods.

关键词： Evidence theory multi-view clustering(MVC) optimization sparse reconstruction

Metagenomics reveals the microbial community and functional metabolism variation in the partial nitritation-anammox process:From collapse to recovery

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Journal of Environmental Sciences 2024年第1期135卷 210-221页

作者： Hong Wang Hui gong Xiaohu Dai Min Yang State Key Laboratory of Pollution Control and Resource Reuse College of Environmental Science and EngineeringTongji UniversityShanghai 200092China BIOMATH Department of Data Analysis and Mathematical ModellingGhent UniversityGhent 9000Belgium

Mainstream partial nitritation-anammox(PNA)process easily suffers from performance instability and even reactor collapse in application.Thus,it is of great significance to unveil the characteristic of performance recovery,understand the intrinsic mechanism and then propose operational strategy.In this study,we combined long-term reactor operation,batch tests,and metagenomics to reveal the succession of microbial community and functional metabolism variation from system collapse to recovery.Proper aeration control(0.10-0.25mg O_(2)/L)was critical for performance recovery.It was also found that Candidatus Brocadia became the dominant flora and its abundance increased from 3.5%to 11.0%.Significant enhancements in carbon metabolism and phospholipid biosynthesis were observed during system recovery,and the genes abundance related to signal transduction was dramatically increased.The up-regulation of sdh and suc genes showed the processes of succinate dehydrogenation and succinyl-CoA synthesis might stimulate the production of amino acids and the synthesis of proteins,thereby possibly improving the activity and abundance of AnAOB,which was conducive to the performance recovery.Moreover,the increase in abundance of hzs and hdh genes suggested the enhancement of the anammox process.Changes in the abundance of key genes involved in nitrogen metabolism indicated that nitrogen removal pathway was more diverse after system recovery.The achievement of performance recovery was driven by anammox,nitrification and denitrification coupled with dissimilatory nitrate reduction to ammonium.These results provide deeper insights into the recovery mechanism of PNA system and also provide a potential regulation strategy for the stable operation of the mainstream PNA process.

关键词： Partial nitritation-anammox Metagenomics Microbial community Functional metabolism Recovery

Predictive model using four ferroptosis-related genes accurately predicts gastric cancer prognosis

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World Journal of Gastrointestinal Oncology 2024年第5期16卷 2018-2037页

作者： Li Wang Wei-Hua gong Department of Emergency The Second Affiliated Hospital of Zhejiang University School of MedicineHangzhou 310000Zhejiang ProvinceChina Department of Surgery The Second Affiliated Hospital Zhejiang University School of MedicineHangzhou 310052Zhejiang ProvinceChina

BACKGROUND Gastric cancer(GC)is a common malignancy of the digestive system.According to global 2018 cancer data,GC has the fifth-highest incidence and the thirdhighest fatality rate among malignant tumors.More than 60%of GC are linked to infection with Helicobacter pylori(H.pylori),a gram-negative,active,microaerophilic,and helical bacterium.This parasite induces GC by producing toxic factors,such as cytotoxin-related gene A,vacuolar cytotoxin A,and outer membrane proteins.Ferroptosis,or iron-dependent programmed cell death,has been linked to GC,although there has been little research on the link between H.pylori infection-related GC and ferroptosis.AIM To identify coregulated differentially expressed genes among ferroptosis-related genes(FRGs)in GC patients and develop a ferroptosis-related prognostic model with discrimination ability.METHODS Gene expression profiles of GC patients and those with H.pylori-associated GC were obtained from The Cancer Genome Atlas and Gene Expression Omnibus(GEO)databases.The FRGs were acquired from the FerrDb database.A ferroptosis-related gene prognostic index(FRGPI)was created using least absolute shrinkage and selection operator–Cox regression.The predictive ability of the FRGPI was validated in the GEO cohort.Finally,we verified the expression of the hub genes and the activity of the ferroptosis inducer FIN56 in GC cell lines and tissues.RESULTS Four hub genes were identified(NOX4,MTCH1,GABARAPL2,and SLC2A3)and shown to accurately predict GC and H.pylori-associated GC.The FRGPI based on the hub genes could independently predict GC patient survival;GC patients in the high-risk group had considerably worse overall survival than did those in the low-risk group.The FRGPI was a significant predictor of GC prognosis and was strongly correlated with disease progression.Moreover,the gene expression levels of common immune checkpoint proteins dramatically increased in the highrisk subgroup of the FRGPI cohort.The hub genes were also confirmed to be highly overexpressed in GC cell line

关键词： Ferroptosis Gastric cancer Helicobacter pylori infection Immune checkpoint protein Prognostic model Ferroptosis-related gene prognostic index

Interplant communication:an emerging battlefield in plant-aphid-virus interactions

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Science China(Life Sciences) 2024年第5期67卷 1069-1071页

作者： Yunjing Wang Qian gong Yule Liu MOE Key Laboratory of Bioinformatics and Center for Plant Biology School of Life SciencesTsinghua UniversityBeijing 100084China Tsinghua-Peking Center for Life Sciences Beijing 100084China

Plants constantly face threats from various organisms,including pathogens and herbivores.Among the pathogens,plant viruses are obligate intracellular parasites that infect plants and cause serious crop yield losses.

关键词： plant Plants communication

Integrative regulatory mechanisms of stomatal movements under changing climate

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Journal of Integrative Plant Biology 2024年第3期66卷 368-393页

作者： Jingbo Zhang Xuexue Chen Yajing Song Zhizhong gong State Key Laboratory of Nutrient Use and Management College of Resources and Environmental SciencesNational Academy of Agriculture Green DevelopmentChina Agricultural UniversityBeijing 100193China State Key Laboratory of Plant Cell and Chromosome Engineering Institute of Genetics and Developmental BiologyChinese Academy of SciencesBeijing 100101China State Key Laboratory of Plant Environmental Resilience College of Biological SciencesChina Agricultural UniversityBeijing 100094China Institute of Life Science and Green Development School of Life SciencesHebei UniversityBaoding 071001China

Global climate change-caused drought stress,high temperatures and other extreme weather profoundly impact plant growth and development,restricting sustainable crop production.To cope with various environmental stimuli,plants can optimize the opening and closing of stomata to balance CO_(2)uptake for photosynthesis and water loss from leaves.Guard cells perceive and integrate various signals to adjust stomatal pores through turgor pressure regulation.Molecular mechanisms and signaling networks underlying the stomatal movements in response to environmental stresses have been extensively studied and elucidated.This review focuses on the molecular mechanisms of stomatal movements mediated by abscisic acid,light,CO_(2),reactive oxygen species,pathogens,temperature,and other phytohormones.We discussed the significance of elucidating the integrative mechanisms that regulate stomatal movements in helping design smart crops with enhanced water use efficiency and resilience in a climate-changing world.

关键词： environmental stress guard cell plant regulatory mechanisms stomatal movements