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Chinese Journal of Chemical Engineering 2024年第7期71卷 24-44页

作者： Chunliang Liu Jianhui Zhong Ranran Wei Jiuxu Ruan Kaicong Wang Zhaoyou Zhu Yinglong Wang Limei Zhong College of Chemical Engineering Qingdao University of Science and TechnologyQingdao 266042China

This work provides an overview of distillation processes,including process design for different distillation processes,selection of entrainers for special distillation processes,system integration and intensification of distillation processes,optimization of process parameters for distillation processes and recent research progress in dynamic control ***,the feasibility of using thermodynamic topological theories such as residual curve,phase equilibrium line and distillation boundary line to analyze different separation regions is discussed,and the rationality of distillation process design is discussed by using its ***,the application of molecular simulation methods such as molecular dynamics simulation and quantum chemical calculation in the screening of entrainer is discussed for the extractive distillation *** thermal coupling mechanism of different distillation processes is used to explore the process of different process ***,a mixed integer nonlinear optimization strategy for the distillation process based on different algorithms is ***,the improvement of dynamic control strategies for different distillation processes in recent years is *** work focuses on the application of process intensification and system optimization in the design of distillation process,and analyzes the challenges,prospects,and development trends of distillation technology in the separation of multicomponent azeotropes.

关键词： Azeotrope separation Process design optimization algorithm Process integration Dynamic control Entrainer selection

optimization Model and algorithm for Multi-Label Learning

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Journal of Applied Mathematics and Physics 2021年第5期9卷 969-975页

作者： Zhengyang Li Fuzhou University Fuzhou China

This paper studies a kind of urban security risk assessment model based on multi-label learning, which is transformed into the solution of linear equations through a series of transformations, and then the solution of linear equations is transformed into an optimization problem. Finally, this paper uses some classical optimization algorithms to solve these optimization problems, the convergence of the algorithm is proved, and the advantages and disadvantages of several optimization methods are compared.

关键词： Operations Research Multi-Label Learning Linear Equations Solving optimization algorithm

optimization Model and algorithm for Multi-Label Learning

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Optimization Model and Algorithm for Multi-Label Learning

Maximizing Resource Efficiency in Cloud Data Centers through Knowledge-Based Flower Pollination algorithm (KB-FPA)

作者： Zhengyang Li Fuzhou University

This paper studies a kind of urban security risk assessment model based on multi-label learning, which is transformed into the solution of linear equations through a series of transformations, and then the solution of linear equations is transformed into an optimization problem. Finally, this paper uses some classical optimization algorithms to solve these optimization problems,the convergence of the algorithm is proved, and the advantages and disadvantages of several optimization methods are compared.

关键词： Operations Research Multi-Label Learning Linear Equations Solving optimization algorithm

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Computers, Materials & Continua 2024年第6期79卷 3757-3782页

作者： Nidhika Chauhan Navneet Kaur Kamaljit Singh Saini Sahil Verma Kavita Ruba Abu Khurma Pedro A.Castillo University Institute of Computing Department Chandigarh UniversityPunjab140413India Department of Computer Science and Engineering Chandigarh UniversityPunjab140413India Universidade Federal do Piauí TeresinaPiauí64049-550Brazil MEU Research Unit Faculty of Information TechnologyMiddle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan Department of Computer Engineering Automatics and RoboticsUniversity of GranadaGranada18071Spain

Cloud computing is a dynamic and rapidly evolving field,where the demand for resources fluctuates *** paper delves into the imperative need for adaptability in the allocation of resources to applications and services within cloud computing *** motivation stems from the pressing issue of accommodating fluctuating levels of user demand *** adhering to the proposed resource allocation method,we aim to achieve a substantial reduction in energy *** reduction hinges on the precise and efficient allocation of resources to the tasks that require those most,aligning with the broader goal of sustainable and eco-friendly cloud computing *** enhance the resource allocation process,we introduce a novel knowledge-based optimization *** this study,we rigorously evaluate its efficacy by comparing it to existing algorithms,including the Flower Pollination algorithm(FPA),Spark Lion Whale optimization(SLWO),and Firefly *** findings reveal that our proposed algorithm,Knowledge Based Flower Pollination algorithm(KB-FPA),consistently outperforms these conventional methods in both resource allocation efficiency and energy consumption *** paper underscores the profound significance of resource allocation in the realm of cloud *** addressing the critical issue of adaptability and energy efficiency,it lays the groundwork for a more sustainable future in cloud computing *** contribution to the field lies in the introduction of a new resource allocation strategy,offering the potential for significantly improved efficiency and sustainability within cloud computing infrastructures.

关键词： Cloud computing resource allocation energy consumption optimization algorithm flower pollination algorithm

The Bedbug Meta-heuristic algorithm to Solve optimization Problems

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Journal of Bionic Engineering 2023年第5期20卷 2465-2485页

作者： Kouroush Rezvani Ali Gaffari Mohammad Reza Ebrahimi Dishabi Department of Computer Engineering Miyaneh BranchIslamic Azad UniversityMiyanehIran Department of Computer Engineering Tabriz BranchIslamic Azad UniversityTabrizIran

Small parasitic Hemipteran insects known as bedbugs(Cimicidae)feed on warm-blooded mammal’s *** most famous member of this family is the Cimex lectularius or common *** current paper proposes a novel swarm intelligence optimization algorithm called the Bedbug Meta-Heuristic algorithm(BMHA).The primary inspiration for the bedbug algorithm comes from the static and dynamic swarming behaviors of bedbugs in *** two main stages of optimization algorithms,exploration,and exploitation,are designed by modeling bedbug social interaction to search for *** proposed algorithm is benchmarked qualitatively and quantitatively using many test functions including *** results of evaluating BMHA prove that this algorithm can improve the initial random population for a given optimization problem to converge towards global optimization and provide highly competitive results compared to other well-known optimization *** results also prove the new algorithm's performance in solving real optimization problems in unknown search *** achieve this,the proposed algorithm has been used to select the features of fake news in a semi-supervised manner,the results of which show the good performance of the proposed algorithm in solving problems.

关键词： Bedbug Meta-Heuristic algorithm optimization algorithm BMHA

A Novel Deep Learning-Based Model for Classification of Wheat Gene Expression

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Computer Systems Science & Engineering 2024年第2期48卷 273-285页

作者： Amr Ismail WalidHamdy Aya MAl-Zoghby Wael AAwad Ahmed Ismail Ebada Yunyoung Nam Byeong-Gwon Kang Mohamed Abouhawwash Faculty of Science Port Said UniversityPort SaidEgypt Modern Academy for Computer Science and Management Technology Cairo11742Egypt Faculty of Computers and Artificial Intelligence Damietta UniversityNew DamiettaEgypt Department of ICT Convergence Soonchunhyang UniversityAsan31538South Korea Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Deep learning(DL)plays a critical role in processing and converting data into knowledge and *** technologies have been applied in a variety of applications,including image,video,and genome sequence *** deep learning the most widely utilized architecture is Convolutional Neural Networks(CNN)are taught discriminatory traits in a supervised *** comparison to other classic neural networks,CNN makes use of a limited number of artificial neurons,therefore it is ideal for the recognition and processing of wheat gene *** is an essential crop of cereals for people around the *** Genotypes identification has an impact on the possible development of many countries in the agricultural *** quantitative genetics prediction of genetic values is a central *** is an allohexaploid(AABBDD)with three distinct *** sizes of the wheat genome are quite large compared to many other kinds and the availability of a diversity of genetic knowledge and normal structure at breeding lines of wheat,Therefore,genome sequence approaches based on techniques of Artificial Intelligence(AI)are *** paper focuses on using the Wheat genome sequence will assist wheat producers in making better use of their genetic resources and managing genetic variation in their breeding program,as well as propose a novel model based on deep learning for offering a fundamental overview of genomic prediction theory and current *** this paper,the hyperparameters of the network are optimized in the CNN to decrease the requirement for manual search and enhance network performance using a new proposed model built on an optimization algorithm and Convolutional Neural Networks(CNN).

关键词： Gene expression convolutional neural network optimization algorithm genomic prediction wheat

Designing thermal demultiplexer: Splitting phonons by negative mass and genetic algorithm optimization

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Chinese Physics B 2021年第3期30卷 36-40页

作者： Yu-Tao Tan Lu-Qin Wang Zi Wang Jiebin Peng Jie Ren Center for Phononics and Thermal Energy Science China-EU Joint Laboratory on NanophononicsShanghai Key Laboratory of Special Artificial Microstructure Materials and TechnologySchool of Physics Science and EngineeringTongji UniversityShanghai 200092China Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 200092China

We propose the concept of thermal demultiplexer, which can split the heat flux in different frequency ranges intodifferent directions. We demonstrate this device concept in a honeycomb lattice with dangling atoms. From the view ofeffective negative mass, we give a qualitative explanation of how the dangling atoms change the original transport *** first design a two-mass configuration thermal demultiplexer, and find that the heat flux can flow into different ports incorresponding frequency ranges roughly. Then, to improve the performance, we choose the suitable masses of danglingatoms and optimize the four-mass configuration with genetic algorithm. Finally, we give out the optimal configuration witha remarkable effect. Our study finds a way to selectively split spectrum-resolved heat to different ports as phonon splitter,which would provide a new means to manipulate phonons and heat, and to guide the design of phononic thermal devices inthe future.

关键词： phonon transport thermal demultiplexer optimization algorithm

Short-term forecasting optimization algorithms for wind speed along Qinghai-Tibet railway based on different intelligent modeling theories

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Journal of Central South University 2009年第4期16卷 690-696页

作者：刘辉田红旗李燕飞 Key Laboratory of Traffic Safety on Track Ministry of Education Central South University School of Traffic and Transportation Engineering Central South University

To protect trains against strong cross-wind along Qinghai-Tibet railway, a strong wind speed monitoring and warning system was developed. And to obtain high-precision wind speed short-term forecasting values for the system to make more accurate scheduling decision, two optimization algorithms were proposed. Using them to make calculative examples for actual wind speed time series from the 18th meteorological station, the results show that: the optimization algorithm based on wavelet analysis method and improved time series analysis method can attain high-precision multi-step forecasting values, the mean relative errors of one-step, three-step, five-step and ten-step forecasting are only 0.30%, 0.75%, 1.15% and 1.65%, respectively. The optimization algorithm based on wavelet analysis method and Kalman time series analysis method can obtain high-precision one-step forecasting values, the mean relative error of one-step forecasting is reduced by 61.67% to 0.115%. The two optimization algorithms both maintain the modeling simple character, and can attain prediction explicit equations after modeling calculation.

关键词： train safety wind speed forecasting wavelet analysis time series analysis Kalman filter optimization algorithm

A new three-term spectral conjugate gradient algorithm with higher numerical performance for solving large scale optimization problems based on Quasi-Newton equation

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International Journal of Modeling, Simulation, and Scientific Computing 2021年第5期12卷 234-247页

作者： Jie Guo Zhong Wan School of Mathematics and Statistics Central South University Hunan ChangshaP.R.China

A new spectral three-term conjugate gradient algorithm in virtue of the Quasi-Newton equation is developed for solving large-scale unconstrained optimization *** is proved that the search directions in this algorithm always satisfy a sufficiently descent condition independent of any line *** convergence is established for general objective functions if the strong Wolfe line search is *** experiments are employed to show its high numerical performance in solving large-scale optimization ***,the developed algorithm is implemented to solve the 100 benchmark test problems from CUTE with different sizes from 1000 to 10,000,in comparison with some similar ones in the *** numerical results demonstrate that our algorithm outperforms the state-of-the-art ones in terms of less CPU time,less number of iteration or less number of function evaluation.

关键词： High performance computing optimization algorithm conjugate gradient method convergence analysis