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A Survey on intelligent optimization Approaches to Boiler Combustion optimization

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CAAI Artificial Intelligence Research 2023年第1期2卷 16-31页

作者： Jing Liang Hao Guo Ke Chen Kunjie Yu Caitong Yue Yunpeng Ma School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001China State Key Laboratory of intelligent Agricultural Power Equipment Luoyang 471000China School of Electrical Engineering and Automation Henan Institute of TechnologyXinxiang 453000China School of Information Engineering Tianjin University of CommerceTianjin 300134China

This paper reviews the researches on boiler combustion optimization,which is an important direction in the field of energy saving and emission *** methods have been used to deal with boiler combustion optimization,among which evolutionary computing(EC)techniques have recently gained much ***,the existing researches are not sufficiently focused and have not been summarized *** has led to slow progress of research on boiler combustion optimization and has obstacles in the *** paper introduces a comprehensive survey of the works of intelligent optimization algorithms in boiler combustion optimization and summarizes the contributions of different optimization ***,this paper discusses new research challenges and outlines future research directions,which can guide boiler combustion optimization to improve energy efficiency and reduce pollutant emission concentrations.

关键词： boiler combustion optimization circulating fluidized bed boiler environmental protection computational intelligence intelligent optimization algorithm

Comparison of differential evolution, particle swarm optimization,quantum-behaved particle swarm optimization, and quantum evolutionary algorithm for preparation of quantum states

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Chinese Physics B 2023年第2期32卷 53-59页

作者：程鑫鲁秀娟刘亚楠匡森 Department of Automation University of Science and Technology of ChinaHefei 230027China Department of Mechanical Engineering The University of Hong KongHong Kong 999077China Quantum Machines Unit Okinawa Institute of Science and Technology Graduate UniversityOkinawa 904-0495Japan

Four intelligent optimization algorithms are compared by searching for control pulses to achieve the preparation of target quantum states for closed and open quantum systems, which include differential evolution(DE), particle swarm optimization(PSO), quantum-behaved particle swarm optimization(QPSO), and quantum evolutionary algorithm(QEA).We compare their control performance and point out their differences. By sampling and learning for uncertain quantum systems, the robustness of control pulses found by these four algorithms is also demonstrated and compared. The resulting research shows that the QPSO nearly outperforms the other three algorithms for all the performance criteria *** conclusion provides an important reference for solving complex quantum control problems by optimization algorithms and makes the QPSO be a powerful optimization tool.

关键词： quantum control state preparation intelligent optimization algorithm

Artificial Intelligence Methods Applied to Catalytic Cracking Processes

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Big Data Mining and Analytics 2023年第3期6卷 361-380页

作者： Fan Yang Mao Xu Wenqiang Lei Jiancheng Lv College of Computer Science Sichuan UniversityChengdu 610041China algorithm and Big Data Center New Hope Liuhe Co LtdChengdu 610000China. Data Intelligence Lab New Hope Liuhe Co LtdChengdu 610000China. College of Computer Science Sichuan UniversityChengdu 610041China.

Fluidic Catalytic Cracking(FCC)is a complex petrochemical process affected by many highly non-linear and interrelated *** yield analysis,flue gas desulfurization prediction,and abnormal condition warning are several key research directions in *** paper will sort out the relevant research results of the existing Artificial Intelligence(AI)algorithms applied to the analysis and optimization of catalytic cracking processes,with a view to providing help for the follow-up *** with the traditional mathematical mechanism method,the AI method can effectively solve the difficulties in FCC process modeling,such as high-dimensional,nonlinear,strong correlation,and large *** methods applied in product yield analysis build models based on massive *** fitting the functional relationship between operating variables and products,the excessive simplification of mechanism model can be avoided,resulting in high model *** methods applied in flue gas desulfurization can be usually divided into two stages:modeling and *** the modeling stage,data-driven methods are often used to build the system model or rule base;In the optimization stage,heuristic search or reinforcement learning methods can be applied to find the optimal operating parameters based on the constructed model or rule *** methods,including data-driven and knowledge-driven algorithms,are widely used in the abnormal condition ***-driven methods have advantages in interpretability and generalization,but disadvantages in construction difficulty and prediction *** the data-driven methods are just the ***,some studies combine these two methods to obtain better results.

关键词： neural networks intelligent optimization algorithm catalytic cracking lumped kinetics

New optimization Design Method for a Double Secondary Linear Motor Based on R-DNN Modeling Method and MCS optimization algorithm

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Chinese Journal of Electrical Engineering 2020年第3期6卷 98-105页

作者： Weitao Wang Jiwen Zhao Yang Zhou Fei Dong School of Electrical Engineering and Automation Anhui UniversityHefei 230601China School of Electrical Engineering and Automation Hefei University of TechnologyHefei 230009China

Traditional linear motor optimization methods typically use analytical models combined with intelligent optimization ***,this approach has disadvantages,e.g.,the analytical model might not be accurate enough,and the intelligent optimization algorithm can easily fall into local optimization.A new linear motor optimization strategy combining an R-deep neural network(R-DNN)and modified cuckoo search(MCS)is proposed;additionally,the thrust lifting and thrust fluctuation reductions are regarded as optimization *** R-DNN is a deep neural network modeling method using the rectified linear unit(RELU)activation function,and the MCS provides a faster convergence speed and stronger data search capability as compared with genetic algorithms,particle swarm optimization,and standard CS ***,the validity and accuracy of this work are proven based on prototype experiments.

关键词： Double secondary linear motor(DSLM) machine learning modeling R-deep neural network(R-DNN)algorithm intelligent optimization algorithm modified cuckoo search(MCS)algorithm

A Spectral Convolutional Neural Network Model Based on Adaptive Fick’s Law for Hyperspectral Image Classification

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Computers, Materials & Continua 2024年第4期79卷 19-46页

作者： Tsu-Yang Wu Haonan Li Saru Kumari Chien-Ming Chen School of Artificial Intelligence(School of Future Technology) Nanjing University of Information Science&TechnologyNanjing210044China College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China Department of Mathematics Chaudhary Charan Singh UniversityMeerutUttar Pradesh250004India

Hyperspectral image classification stands as a pivotal task within the field of remote sensing,yet achieving highprecision classification remains a significant *** response to this challenge,a Spectral Convolutional Neural Network model based on Adaptive Fick’s Law algorithm(AFLA-SCNN)is *** Adaptive Fick’s Law algorithm(AFLA)constitutes a novel metaheuristic algorithm introduced herein,encompassing three new strategies:Adaptive weight factor,Gaussian mutation,and probability update *** adaptive weight factor,the algorithmcan adjust theweights according to the change in the number of iterations to improve the performance of the *** helps the algorithm avoid falling into local optimal solutions and improves the searchability of the *** probability update strategy helps to improve the exploitability and adaptability of the *** the AFLA-SCNN model,AFLA is employed to optimize two hyperparameters in the SCNN model,namely,“numEpochs”and“miniBatchSize”,to attain their optimal ***’s performance is initially validated across 28 functions in 10D,30D,and 50D for CEC2013 and 29 functions in 10D,30D,and 50D for *** results indicate AFLA’s marked performance superiority over nine other prominent optimization ***,the AFLA-SCNN model was compared with the Spectral Convolutional Neural Network model based on Fick’s Law algorithm(FLA-SCNN),Spectral Convolutional Neural Network model based on Harris Hawks optimization(HHO-SCNN),Spectral Convolutional Neural Network model based onDifferential Evolution(DE-SCNN),SpectralConvolutionalNeuralNetwork(SCNN)model,and SupportVector Machines(SVM)model using the Indian Pines dataset and PaviaUniversity *** experimental results show that the AFLA-SCNN model outperforms other models in terms of Accuracy,Precision,Recall,and F1-score on Indian Pines and Pavia *** them,the Accuracy of the AFLA-SCNN model on India

关键词： Adaptive Fick’s law algorithm spectral convolutional neural network metaheuristic algorithm intelligent optimization algorithm hyperspectral image classification

基于智能优化算法的变分模态分解参数优化对比研究

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建模与仿真 2024年第6期13卷 6009-6018页

作者：楼志鹏杨丹丹陈思源赵军孔维宾邵宇成盐城工学院优培学院江苏盐城盐城工学院信息工程学院江苏盐城

变分模态分解(Variational Mode Decomposition, VMD)作为一种有效的信号分解工具，已被广泛应用。然而，VMD的分解效果高度依赖于其参数的选择，参数优化对提高信号分解能力至关重要。本研究旨在通过智能优化算法对VMD的关键参数进行... 详细信息

变分模态分解(Variational Mode Decomposition, VMD)作为一种有效的信号分解工具，已被广泛应用。然而，VMD的分解效果高度依赖于其参数的选择，参数优化对提高信号分解能力至关重要。本研究旨在通过智能优化算法对VMD的关键参数进行优化，并对比不同算法的性能表现。具体而言，本文采用了海洋掠食者算法、小龙虾算法和非洲秃鹰优化算法来优化VMD的参数，并将其应用于机械故障诊断。通过对比实验结果，分析各算法在故障诊断中的应用效果与优劣，为VMD参数优化提供了理论依据和实践指导。

关键词：变分模态分解参数优化智能优化算法故障诊断 Variational Mode Decomposition Parameter optimization intelligent optimization algorithm Fault Diagnosis

Optimal Search Mechanism Analysis of Light Ray optimization algorithm

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Journal of Mathematical Research with Applications 2012年第5期32卷 530-542页

作者： Jihong SHEN1,Jialian LI2,,Bin WEI1 1.College of Science,Harbin Engineering University,Heilongjiang 150001,P.R.China 2.College of Automation,Harbin Engineering University,Heilongjiang 150001,P.R.China College of Science Harbin Engineering University College of Automation Harbin Engineering University

Based on Fermat's principle and the automatic optimization mechanism in the propagation process of light,an optimal searching algorithm named light ray optimization is presented,where the laws of refraction and reflection of light rays are integrated into searching process of *** this algorithm,coordinate space is assumed to be the space that is full of media with different refractivities,then the space is divided by grids,and finally the searching path is assumed to be the propagation path of light *** the law of refraction,the search direction is deflected to the direction that makes the value of objective function *** the law of reflection,the search direction is changed,which makes the search continue when it cannot keep going with *** the function values of objective problems are used and there is no artificial rule in light ray optimization,so it is simple and easy to *** analysis and the results of numerical experiments show that the algorithm is feasible and effective.

关键词： Fermat’s principle intelligent optimization algorithm light ray optimization optimal search mechanism.

Rapid Determination of Hemicellulose Content in Corn Stalks by Near-infrared Spectroscopy Based on Dung Beetle Optimizer

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Agricultural Biotechnology 2024年第5期13卷 83-85,92页

作者： Baihong TONG Jinming LIU Jianfei SHI College of Information and Electrical Engineering Heilongjiang Bayi Agricultural UniversityDaqing 163000China

Corn stalks are a kind of common organic fertilizer and feed material in agriculture in China,as well as an important source of modern biomass energy and new *** is an important component in corn stalks,and it is very important to determine its content in corn *** this paper,the feasibility of near-infrared spectroscopy(NIRS)combined with chemometrics for rapid detection of hemicellulose content in corn stalks was *** order to improve the accuracy of NIRS detection,a new intelligent optimization algorithm,dung beetle optimizer(DBO),was applied to select characteristic wavelengths of *** modeling performance was compared with that based on characteristic wavelength selection using genetic algorithm(GA)and binary particle swarm optimization(BPSO),and it was found that the characteristic wavelength selection performance of DBO was excellent,and the regression accuracy of hemicellulose quantitative detection model established by its preferred characteristic wavelengths was better than the above two intelligent optimization algorithms.

关键词： Hemicellulose Near-infrared spectrum Characteristic wavelength selection intelligent optimization algorithm Dung beetle algorithm

Improved Particle Swarm optimization for Structural Engineering Design

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Improved Particle Swarm Optimization for Structural Engineer...