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Research on Intelligent Optimization Technology of Boiler Combustion Parameters in Panshan Power Plant Under Low-Nitrogen Emission Reduction Conditions 认领 引用
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作者 WANG Xisen 《外文科技期刊数据库（文摘版）工程技术》 2026年第2期053-058,共6页
Against the backdrop of increasingly stringent ultra-low emission standards in the thermal power industry, the traditional manual regulation mode of boiler This study takes the coal‑fired boiler of Panshan Power Plant... Against the backdrop of increasingly stringent ultra-low emission standards in the thermal power industry, the traditional manual regulation mode of boiler This study takes the coal‑fired boiler of Panshan Power Plant as the research object. Centering on the core objective of low‑nitrogen emission reduction and combined with the actual operating conditions of the unit, it analyzes the influencing mechanism of boiler combustion parameters on NOₓ formation, furnace combustion stability and boiler thermal efficiency. By virtue of big‑data acquisition and intelligent algorithms, a multi‑objective intelligent optimization model for boiler combustion parameters is constructed, and dynamic optimization research is conducted on key parameters such as excess air coefficient, primary‑secondary air ratio, burner tilt angle and furnace oxygen content.Comparative analyses of field tests and data simulations verify that the optimized intelligent regulation system can effectively suppress the generation of thermal NOₓ and fuel‑NOₓ in the furnace, drastically reduce flue gas pollutant emissions, improve in‑furnace combustion conditions and lower incomplete combustion losses. Test results demonstrate that after the implementation of the intelligent optimization scheme, NOₓ emission concentrations decrease significantly across all load ranges of the unit, and boiler thermal efficiency is improved effectively. This research resolves industrial pain points of traditional manual adjustment including low precision, prominent hysteresis and poor working‑condition adaptability, and provides technical reference and practical experience for low‑nitrogen and high‑efficiency combustion optimization of similar thermal power generating units. 展开更多
关键词 low-nitrogen emission reduction power plant boiler combustion parameters intelligent optimization thermal efficiency working condition regulation
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An Intelligent Optimization Strategy for Blast Furnace Charging Operation Considering Three-Dimensional Burden Surface Shape 认领 引用 被引量：1
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作者 Jicheng Zhu Zhaohui Jiang +4 位作者 Dong Pan Haoyang Yu Chuan Xu Ke Zhou Weihua Gui 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第7期1445-1463,共19页
Today,a well-devised charging operation scheme is urgently needed by on-site workmen and is critical for building an intelligent blast furnace(BF).Previous research on charging operations always focused on the two-dim... Today,a well-devised charging operation scheme is urgently needed by on-site workmen and is critical for building an intelligent blast furnace(BF).Previous research on charging operations always focused on the two-dimensional shape of the burden surface(i.e.,a single radial profile)while neglecting the unique feature of global dissymmetry,severely restricting the development of precise charging.For this reason,this study proposes an innovative optimization strategy for the charging operation under the three-dimensional burden surface,which is the first attempt in this field.First,a practicable region partitioning scheme is introduced,and the partitioning results are then integrated with the charging mechanism to construct a three-dimensional burden surface prediction model.Next,the intrinsic relationship between the operational parameters and charging volume is revealed based on the law of mass conservation,which forms the basis for defining a novel operational parameter with variable-speed utility,referred to as the neotype charging matrix(NCM).To find the best NCM,a customized NCM optimization strategy,involving a dual constraint handling technique in conjunction with a two-stage hybrid variable differential evolution algorithm,is further developed.The industrial experiment results manifest that the partitioning scheme significantly enhances the accuracy of burden surface description.Moreover,the NCM optimization strategy offers greater flexibility and higher accuracy than current mainstream optimization strategies for the charging matrix(CM). 展开更多
关键词 Blast furnace ironmaking process charging operation intelligent optimization strategy three-dimensional burden surface
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Intelligent optimization method of fracturing parameters for shale oil reservoirs in Jimsar Sag,Junggar Basin,NW China 认领 引用
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作者 WANG Yunjin ZHOU Fujian +5 位作者 SU Hang ZHENG Leyi LI Minghui YU Fuwei LI Yuan LIANG Tianbo 《Petroleum Exploration and Development》 SCIE 2025年第3期830-841,共12页
For shale oil reservoirs in the Jimsar Sag of Junggar Basin,the fracturing treatments are challenged by poor prediction accuracy and difficulty in parameter optimization.This paper presents a fracturing parameter inte... For shale oil reservoirs in the Jimsar Sag of Junggar Basin,the fracturing treatments are challenged by poor prediction accuracy and difficulty in parameter optimization.This paper presents a fracturing parameter intelligent optimization technique for shale oil reservoirs and verifies it by field application.A self-governing database capable of automatic capture,storage,calls and analysis is established.With this database,22 geological and engineering variables are selected for correlation analysis.A separated fracturing effect prediction model is proposed,with the fracturing learning curve decomposed into two parts:(1)overall trend,which is predicted by the algorithm combining the convolutional neural network with the characteristics of local connection and parameter sharing and the gated recurrent unit that can solve the gradient disappearance;and(2)local fluctuation,which is predicted by integrating the adaptive boosting algorithm to dynamically adjust the random forest weight.A policy gradient-genetic-particle swarm algorithm is designed,which can adaptively adjust the inertia weights and learning factors in the iterative process,significantly improving the optimization ability of the optimization strategy.The fracturing effect prediction and optimization strategy are combined to realize the intelligent optimization of fracturing parameters.The field application verifies that the proposed technique significantly improves the fracturing effects of oil wells,and it has good practicability. 展开更多
关键词 Jimsar Sag shale oil fracturing parameter learning curve intelligent optimization reinforcement learning particle swarm algorithm
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APPROACH ON INTELLIGENT OPTIMIZATION DESIGN BASED ON COMPOUND KNOWLEDGE 认领 引用 被引量：1
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作者 Yao Jianchu Zhou JiYu JunSchool of Mechanical Scienceand Engineering,Huazhong University of Scienceand Technology,Wuhan 430074, China 《Chinese Journal of Mechanical Engineering》 EI CAS 2003年第3期300-304,共5页
A concept of an intelligent optimal design approach is proposed, which isorganized by a kind of compound knowledge model. The compound knowledge consists of modularizedquantitative knowledge, inclusive experience know... A concept of an intelligent optimal design approach is proposed, which isorganized by a kind of compound knowledge model. The compound knowledge consists of modularizedquantitative knowledge, inclusive experience knowledge and case-based sample knowledge. By usingthis compound knowledge model, the abundant quantity information of mathematical programming and thesymbolic knowledge of artificial intelligence can be united together in this model. The intelligentoptimal design model based on such a compound knowledge and the automatically generateddecomposition principles based on it are also presented. Practically, it is applied to theproduction planning, process schedule and optimization of production process of a refining &chemical work and a great profit is achieved. Specially, the methods and principles are adaptablenot only to continuous process industry, but also to discrete manufacturing one. 展开更多
关键词 Intelligent optimization Automatic modeling Compound knowledge Expertsystem
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Intelligent optimization methods of phase-modulation waveform 认领 引用 被引量：1
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作者 SUN Jianwei WANG Chao +3 位作者 SHI Qingzhan REN Wenbo YAO Zekun YUAN Naichang 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2022年第4期916-923,共8页
With the continuous improvement of radar intelligence, it is difficult for traditional countermeasures to achieve ideal results. In order to deal with complex, changeable, and unknown threat signals in the complex ele... With the continuous improvement of radar intelligence, it is difficult for traditional countermeasures to achieve ideal results. In order to deal with complex, changeable, and unknown threat signals in the complex electromagnetic environment, a waveform intelligent optimization model based on intelligent optimization algorithm is proposed. By virtue of the universality and fast running speed of the intelligent optimization algorithm, the model can optimize the parameters used to synthesize the countermeasure waveform according to different external signals, so as to improve the countermeasure performance.Genetic algorithm(GA) and particle swarm optimization(PSO)are used to simulate the intelligent optimization of interruptedsampling and phase-modulation repeater waveform. The experimental results under different radar signal conditions show that the scheme is feasible. The performance comparison between the algorithms and some problems in the experimental results also provide a certain reference for the follow-up work. 展开更多
关键词 waveform optimization intelligent optimization phase-modulation genetic algorithm(GA) particle swarm optimization(PSO)
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A Fixed-Point Iterative Method for Discrete Tomography Reconstruction Based on Intelligent Optimization 认领 引用
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作者 Luyao Yang Hao Chen +2 位作者 Haocheng Yu Jin Qiu Shuxian Zhu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第1期731-745,共15页
Discrete Tomography(DT)is a technology that uses image projection to reconstruct images.Its reconstruction problem,especially the binary image(0–1matrix)has attracted strong attention.In this study,a fixed point iter... Discrete Tomography(DT)is a technology that uses image projection to reconstruct images.Its reconstruction problem,especially the binary image(0–1matrix)has attracted strong attention.In this study,a fixed point iterative method of integer programming based on intelligent optimization is proposed to optimize the reconstructedmodel.The solution process can be divided into two procedures.First,the DT problem is reformulated into a polyhedron judgment problembased on lattice basis reduction.Second,the fixed-point iterativemethod of Dang and Ye is used to judge whether an integer point exists in the polyhedron of the previous program.All the programs involved in this study are written in MATLAB.The final experimental data show that this method is obviously better than the branch and bound method in terms of computational efficiency,especially in the case of high dimension.The branch and bound method requires more branch operations and takes a long time.It also needs to store a large number of leaf node boundaries and the corresponding consumptionmatrix,which occupies a largememory space. 展开更多
关键词 Discrete tomography integer programming fixed-point iterative algorithm intelligent optimization lattice basis reduction
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Comparative Analysis for Evaluating Wind Energy Resources Using Intelligent Optimization Algorithms and Numerical Methods 认领 引用
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作者 Musaed Alrashidi 《Computer Systems Science & Engineering》 SCIE EI 2023年第10期491-513,共23页
Statistical distributions are used to model wind speed,and the twoparameters Weibull distribution has proven its effectiveness at characterizing wind speed.Accurate estimation of Weibull parameters,the scale(c)and sha... Statistical distributions are used to model wind speed,and the twoparameters Weibull distribution has proven its effectiveness at characterizing wind speed.Accurate estimation of Weibull parameters,the scale(c)and shape(k),is crucial in describing the actual wind speed data and evaluating the wind energy potential.Therefore,this study compares the most common conventional numerical(CN)estimation methods and the recent intelligent optimization algorithms(IOA)to show how precise estimation of c and k affects the wind energy resource assessments.In addition,this study conducts technical and economic feasibility studies for five sites in the northern part of Saudi Arabia,namely Aljouf,Rafha,Tabuk,Turaif,and Yanbo.Results exhibit that IOAs have better performance in attaining optimal Weibull parameters and provided an adequate description of the observed wind speed data.Also,with six wind turbine technologies rating between 1 and 3MW,the technical and economic assessment results reveal that the CN methods tend to overestimate the energy output and underestimate the cost of energy($/kWh)compared to the assessments by IOAs.The energy cost analyses show that Turaif is the windiest site,with an electricity cost of$0.016906/kWh.The highest wind energy output is obtained with the wind turbine having a rated power of 2.5 MW at all considered sites with electricity costs not exceeding$0.02739/kWh.Finally,the outcomes of this study exhibit the potential of wind energy in Saudi Arabia,and its environmental goals can be acquired by harvesting wind energy. 展开更多
关键词 Weibull distribution conventional numerical methods intelligent optimization algorithms wind resource exploration and exploitation cost of energy($/kWh)
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Research on the Application of Intelligent Optimization Algorithm in Mechanical Design 认领 引用
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作者 Donglai LUAN 《Mechanical Engineering Science》 2024年第1期26-29,共4页
Intelligent optimization algorithm belongs to a kind of emerging technology,show good characteristics,such as high performance,applicability,its algorithm includes many contents,including genetic,particle swarm and ar... Intelligent optimization algorithm belongs to a kind of emerging technology,show good characteristics,such as high performance,applicability,its algorithm includes many contents,including genetic,particle swarm and artificial neural network algorithm,compared with the traditional optimization way,these algorithms can be applied to a variety of situations,meet the demand of solution,in the mechanical design industry has wide application prospects.This paper analyzes the application of the algorithm in mechanical design and the comparison of the results to verify the significance of the intelligent optimization algorithm in mechanical design. 展开更多
关键词 intelligent optimization algorithm mechanical design application
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A diffusion model-based intelligent optimization method of rural road environments 认领 引用
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作者 Bo Yu Zehong Zhu +3 位作者 Yuren Chen Junhua Wang Kun Gao Xin Qian 《International Journal of Transportation Science and Technology》 EI CSCD 2026年第1期208-227,共20页
Well-designed rural road environments can guide drivers to adopt reasonable driving behaviors,thereby significantly improving the driving experience and ensuring road safety.Existing methods for optimizing rural road ... Well-designed rural road environments can guide drivers to adopt reasonable driving behaviors,thereby significantly improving the driving experience and ensuring road safety.Existing methods for optimizing rural road environments mainly rely on expert knowledge,have low automation degrees,and are limited in efficiency and accuracy.Therefore,this study aims to propose an intelligent optimization method for rural road environments by using image generation technology.Using environment images from a naturalistic driving dataset,the area and location information of semantic components(e.g.,lane markings,vegetation,guardrails,and traffic signs)in rural road environments are extracted,and their impacts on driving speed are analyzed based on explainable machine learning(extreme gradient boosting(XGBoost)and Shapley additive explanations(SHAP)).These impacts are then utilized to determine how to adjust and optimize the road environment components at appropriate locations(i.e.,obtain the optimization scheme).Then,a novel image generation technique,Diffusion model,is employed to establish an intelligent optimization method,which can directly generate optimized images of rural road environments.Compared with traditional manual mapping and other popular image generation algorithms such as CycleGAN,the method proposed in this study has the advantages of high efficiency,labor saving,and superior image generation quality.This study can facilitate the design and optimization of rural road environments and enhance rural road safety in a more intelligent way. 展开更多
关键词 Diffusion model Intelligent optimization Image generation technology Rural road environments Explainable machine learning
A new intelligent optimization method for drilling parameters of extended reach wells based on mechanical specific energy and machine learning 认领 引用 被引量：1
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作者 Xuyue Chen Rong Wang +3 位作者 Jin Yang Deli Gao Gengchen Li Pengbo Li 《Petroleum Research》 EI CAS CSCD 2025年第3期485-500,共16页
Extended reach wells(ERWs)can efficiently develop offshore satellite oilfields,reduce development costs and improve economic benefits.However,owing to the complex geological conditions,it is difficult to determine the... Extended reach wells(ERWs)can efficiently develop offshore satellite oilfields,reduce development costs and improve economic benefits.However,owing to the complex geological conditions,it is difficult to determine the drilling parameters of extended reach drilling,which greatly restricts the rate of penetration(ROP)and increases the cost of drilling operations.In this paper,a new intelligent optimization method for drilling parameters of ERWs based on mechanical specific energy(MSE)and machine learning is proposed.Unlike conventional approaches,this method combines an ensemble regression(ER)model for predicting ROP with the non-dominated sorting genetic algorithm-II(NSGA-II)to optimize multiple objectives,including MSE,ROP,and unit footage cost(UFC).The results show that through the intelligent optimization of drilling parameters for extended reach drilling wells in Block M of Bohai Oilfield,the two decision variables of the weight on bit(WOB)and rotations per minute(RPM)are increased,MSE is constantly converging or even equal to the confined compressive strength(CCS)of the rock,UFC is reduced by nearly 51.57%,and ROP is increased by approximately 31.88%.The findings demonstrate the effectiveness of the approach in enhancing drilling efficiency and reducing operational costs,offering an innovative solution for the optimization of drilling parameters in ERWs. 展开更多
关键词 Extended reach drilling Intelligent optimization of drilling parameters Rate of penetration prediction Mechanical specific energy Machine learning NSGA-II
Resilient Photovoltaics:Global Optimization and Advanced Control under Complex Operating Conditions:A Critical Review 认领 引用
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作者 Wulfran Fendzi Mbasso Idriss Dagal +2 位作者 Manish Kumar Singla Muhammad Suhail Shaikh Aseel Smerat 《Energy Engineering》 EI 2026年第3期247-286,共40页
Utility-scale PV plants increasingly operate under partial shading,soiling,temperature swings,and rapid irradiance ramps that depress yield and challenge stability on weak grids.This critical review addresses those co... Utility-scale PV plants increasingly operate under partial shading,soiling,temperature swings,and rapid irradiance ramps that depress yield and challenge stability on weak grids.This critical review addresses those conditions by(i)unifying a stressor-to-method taxonomy that links field stressors to global intelligent MPPT(metaheuristics and learning-based trackers)and to advanced inverter controls(adaptive/MPC and grid-forming),(ii)standardizing metrics and reporting aligned with IEC 61724-1 and IEEE 1547/1547.1 to enable fair,reproducible comparisons,and(iii)framing MPPT and grid support as a co-design problem with a DT→HIL→Field validation pathway and seedable scenarios.We identify persistent gaps—fragmented partial-shading benchmarks,limited low-SCR testing,and scarce field-grade validation—and compile a quantitative synthesis:global soiling typically reduces annual production by≈3%–5%,and hybrid/learning MPPT frequently report≈99%tracking efficiency under PSC in simulation/HIL studies.To demonstrate practical relevance,we validate the framework on a seeded scenario library:DRL trackers achieve medianηMPPT≈0.996 with t95≈0.19 s and Hybrid trackers≈0.992/0.26 s,outperforming Metaheuristics(≈0.984/0.42 s);at SCR=2.5,grid-forming control raises VRI from~0.78(tuned GFL)to~0.95 while keeping THD within 2.5%–3.2%,with all stacks meeting IEEE-1547.1 Category-II ride-through.The resulting taxonomy,standards-aligned reporting,and open seeds provide a replicable basis for comparable,grid-relevant benchmarking and clear guidance for real-world design and operations. 展开更多
关键词 Photovoltaic(PV)systems intelligent optimization maximum power point tracking(MPPT)under partial shading grid-forming control weak-grid resilience
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Improved Artificial Rabbit Optimization Algorithm Fused with Particle Swarm Optimization for Wireless Sensor Network Coverage Optimization 认领 引用 被引量：1
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作者 WU Jin SU Zhengdong 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第2期375-389,共15页
Aiming at the problem of low node coverage during node deployment in wireless sensor network(WSN),an improved artificial rabbit optimization algorithm incorporating particle swarm optimization(ARO-PSO)is proposed for ... Aiming at the problem of low node coverage during node deployment in wireless sensor network(WSN),an improved artificial rabbit optimization algorithm incorporating particle swarm optimization(ARO-PSO)is proposed for network coverage optimization.ARO-PSO successfully combines the stochastic characteristics of ARO and the global characteristics of PSO.Firstly,to optimize the quality of the initial population,Sine chaos mapping is introduced to initialize the population;secondly,to better balance the exploration and exploitation,adaptive settings are made;finally,combined with the characteristics of the ARO energy factor,a population decreasing strategy is introduced to further accelerate the convergence speed of the algorithm.Experimental and analytical comparisons are made with ARO and PSO and 6 other excellent optimizers on 13 benchmark functions.The results show that ARO-PSO largely outperforms the original algorithm.Finally,ARO-PSO is applied to WSN coverage optimization experiments in 2D and 3D environments,and the proposed algorithm exhibits higher network coverage and improves the monitoring quality of the network compared to standard ARO and PSO and other state-of-the-art algorithms.The experimental results fully demonstrate the superiority of the ARO-PSO-based WSN node deployment optimization method. 展开更多
关键词 wireless sensor network(WSN) swarm intelligence optimization artificial rabbits optimization(ARO) particle swarm optimization(PSO) coverage optimization
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Intelligent prediction of deep rock strength based on modified three-dimensional Hoek-Brown criterion 认领 引用
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作者 Jiaxin Wang Hongru Jiang +4 位作者 Shunchuan Wu Shihuai Zhang Chaoqun Chu Xiaoping Zhang Yingming Xiao 《International Journal of Minerals,Metallurgy and Materials》 SCIE EI CAS CSCD 2026年第7期2454-2472,I0020-I0030,共19页
To study the deep rock strength,this paper proposes a five-parameter deviatoric function to modify the deviatoric function of the Hoek-Brown(HB)criterion introduces an intelligent optimization algorithm(IOA)to determi... To study the deep rock strength,this paper proposes a five-parameter deviatoric function to modify the deviatoric function of the Hoek-Brown(HB)criterion introduces an intelligent optimization algorithm(IOA)to determine the material parameters,thereby constructing a modified three-dimensional(3D)HB criterion,namely MMCHB criterion.The MMCHB criterion avoids the defects of the traditional HB criterion,which neither considers the Intermediate principal stress(IPS)nor meets the smoothness requirement,and overcomes the shortcomings of parameter determination based on conventional methods,which can lead to a single deviatoric plane envelope shape.This modified criterion can be degenerated into the HB criterion under triaxial compression and tension.The proposed criterion is verified using true triaxial test data for six types of intact rock,and the modified 3D HB criteria are selected for comparative study.The results show that the proposed criterion under the IOA has the best prediction error for the six rock types,ranging from 1.6636% to 3.4023%.Overall,the MMCHB criterion outperforms the existing modified 3D HB criteria in prediction.Based on the proposed MMCHB criterion,an intelligent prediction system is developed,which provides a new approach for intelligent prediction of deep rock strength and dynamic construction of rock material parameters. 展开更多
关键词 rock strength Hoek-Brown criterion Intermediate principal stress intelligent optimization algorithm intelligent predictionsystem
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Novel Sea Otter Optimization Algorithm for WSN Coverage Intelligence Optimization 认领 引用
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作者 WU Jin GAO Yaqiong +2 位作者 SU Zhengdong CHONG Gege XIONG Hao 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第4期828-842,I0002,共15页
A novel intelligent optimization algorithm inspired by nature,called sea otter optimization algorithm(SOOA),is proposed.The SOOA simulates the natural behaviors of sea otters,such as using tactile senses to search for... A novel intelligent optimization algorithm inspired by nature,called sea otter optimization algorithm(SOOA),is proposed.The SOOA simulates the natural behaviors of sea otters,such as using tactile senses to search for food in seawater,grooming their fur,feeding with the aid of stones,and escaping from danger.In the exploration stage,a wetness factor is introduced to control the behavior of sea otters in foraging and grooming;a danger factor is introduced to control the behavior of sea otters in feeding and avoiding dangers in the exploitation stage,and the behaviors of sea otters in responding to different dangers are mathematically modeled.The proposed algorithm is compared with 9 well-known intelligent optimization algorithms,and evaluated in 13 benchmark functions as well as wireless sensor network coverage optimization problems to verify the effectiveness of the proposed algorithm.The experimental results show that the node coverage after SOOA optimization reaches 91.2%in 2D environment and 90.47%in 3D environment.Compared with other algorithms,SOOA is superior and possesses the ability to solve complex optimization problems. 展开更多
关键词 sea otter optimization algorithm(SOOA) swarm intelligence optimization wireless sensor network coverage optimization
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A study on the credit risk of commercial banks based on intelligent optimization algorithms to modify the KMV model 认领 引用
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作者 Chaojie Fan 《Journal of Applied Economics and Policy Studies》 2025年第6期1-9,共9页
Credit risk is one of the main risks faced by commercial banks.Credit risk management includes risk identification,assessment,and early warning,among which risk assessment is fundamental and key.Currently,research on ... Credit risk is one of the main risks faced by commercial banks.Credit risk management includes risk identification,assessment,and early warning,among which risk assessment is fundamental and key.Currently,research on credit risk assessment in China is still in its developing stage,and the precision of measuring credit risk needs improvement.Among various evaluation methods,the Kealhofer,McQuown,and Vasicek model(KMV model)has shown good practical application and is relatively suitable for the national conditions of China.However,it still has some flaws.To address the issue of insufficient external validity in the default point parameter settings of the KMV model,the Particle Swarm Optimization(PSO)algorithm is used to optimize these parameters,and the Particle Swarm Optimization-Grey Wolf Optimization(PSO-GWO)algorithm is integrated to construct the Adaptive Particle Swarm Optimization-KMV Model(APSO-KMV model)and the PSO-GWO-KMV model.Based on an empirical study comparing real data from 5,234 companies,it was found that the original KMV model had an Area Under the Curve(AUC)value of 0.7362,accuracy of 0.2610,and binary cross-entropy loss of 0.7006;the PSO-KMV model had a shortterm debt coefficient?of 0.0496,a long-term debt coefficient?of 0.2508,an AUC value of 0.9994,accuracy of 0.9996,and binary cross-entropy loss of 4.1990;the PSO-GWO-KMV model had a coefficient?of 0.0496 and a value?of 0.2690,an AUC value of 0.9987,accuracy of 0.7603,and binary cross-entropy loss of 4.0804.The optimized KMV model showed a significant improvement in predictive accuracy. 展开更多
关键词 commercial banks credit risk KMV model intelligent optimization algorithms
Nonlinear Equations Solving with Intelligent Optimization Algorithms: A Survey 认领 引用 被引量：23
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作者 Wenyin Gong Zuowen Liao +2 位作者 Xianyan Mi Ling Wang Yuanyuan Guo 《Complex System Modeling and Simulation》 2021年第1期15-32,共18页
Nonlinear Equations(NEs),which may usually have multiple roots,are ubiquitous in diverse fields.One of the main purposes of solving NEs is to locate as many roots as possible simultaneously in a single run,however,it ... Nonlinear Equations(NEs),which may usually have multiple roots,are ubiquitous in diverse fields.One of the main purposes of solving NEs is to locate as many roots as possible simultaneously in a single run,however,it is a difficult and challenging task in numerical computation.In recent years,Intelligent Optimization Algorithms(IOAs)have shown to be particularly effective in solving NEs.This paper provides a comprehensive survey on IOAs that have been exploited to locate multiple roots of NEs.This paper first revisits the fundamental definition of NEs and reviews the most recent development of the transformation techniques.Then,solving NEs with IOAs is reviewed,followed by the benchmark functions and the performance comparison of several state-of-the-art algorithms.Finally,this paper points out the challenges and some possible open issues for solving NEs. 展开更多
关键词 Nonlinear Equations(NEs) Intelligent Optimization Algorithms(IOA) multiple roots location transformation techniques diversity preservation
A Survey on Intelligent Optimization Approaches to Boiler Combustion Optimization 认领 引用
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作者 Jing Liang Hao Guo +3 位作者 Ke Chen Kunjie Yu Caitong Yue Yunpeng Ma 《CAAI Artificial Intelligence Research》 2023年第1期16-31,共16页
This paper reviews the researches on boiler combustion optimization,which is an important direction in the field of energy saving and emission reduction.Many methods have been used to deal with boiler combustion optim... This paper reviews the researches on boiler combustion optimization,which is an important direction in the field of energy saving and emission reduction.Many methods have been used to deal with boiler combustion optimization,among which evolutionary computing(EC)techniques have recently gained much attention.However,the existing researches are not sufficiently focused and have not been summarized systematically.This has led to slow progress of research on boiler combustion optimization and has obstacles in the application.This paper introduces a comprehensive survey of the works of intelligent optimization algorithms in boiler combustion optimization and summarizes the contributions of different optimization algorithms.Finally,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. 展开更多
关键词 boiler combustion optimization circulating fluidized bed boiler environmental protection computational intelligence intelligent optimization algorithm
Knowledge Classification-Assisted Evolutionary Multitasking for Two-Task Multiobjective Optimization Problems 认领 引用
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作者 Xiaoling Wang Qi Kang +3 位作者 MengChu Zhou Qi Deng Zheng Fan Haoyue Liu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第6期1176-1193,共18页
To realize Industry 5.0,manufacturers face various optimization problems that seldom appear in isolation.Evolutionary MultiTasking(EMT)is an effective method to solve multiple related problems by extracting and utiliz... To realize Industry 5.0,manufacturers face various optimization problems that seldom appear in isolation.Evolutionary MultiTasking(EMT)is an effective method to solve multiple related problems by extracting and utilizing common knowledge.Knowledge transfer is the key to the effectiveness of EMT.Existing EMT methods mainly focus on designing effective intertask learning methods and ignore the fact that provided knowledge's appropriateness also has a significant effect on EMT's performance.There is plentiful knowledge in assistant tasks,and knowledge transfer may not work well and even lead to a negative effect if useless knowledge is selected to guide target tasks.EMT is thus confronted with a challenge to find appropriate knowledge.This work proposes an efficient knowledge classification-assisted EMT framework to identify and select valuable knowledge from assistant tasks.During the evolution process,better-performing candidates are supposed to have advantages in exploitation.Therefore,assistant individuals that are similar to better-performing target individuals are used to provide positive knowledge.Specifically,the target sub-population is divided into different levels and then a classifier is trained to divide assistant sub-population.Considering that target and assistant sub-populations have different characteristics,we use domain adaptation to reduce their distribution discrepancies.In this way,the trained classifier can classify assistant individuals more accurately,and truly useful knowledge can be selected for target tasks.The superior performance of our proposed framework over state-of-the-art algorithms is verified via a series of benchmark problems. 展开更多
关键词 Artificial intelligence evolutionary multitasking intelligent optimization inter-task learning knowledge classification knowledge transfer machine learning multiobjective optimization
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Optimization model for performance-based warranty decision of degraded systems based on improved sparrow search algorithm 认领 引用
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作者 DONG Enzhi CHENG Zhonghua +3 位作者 LIU Zichang ZHU Xi WANG Rongcai BAI Yongsheng 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2025年第5期1259-1280,共22页
Performance-based warranties(PBWs)are widely used in industry and manufacturing.Given that PBW can impose financial burdens on manufacturers,rational maintenance decisions are essential for expanding profit margins.Th... Performance-based warranties(PBWs)are widely used in industry and manufacturing.Given that PBW can impose financial burdens on manufacturers,rational maintenance decisions are essential for expanding profit margins.This paper proposes an optimization model for PBW decisions for systems affected by Gamma degradation processes,incorporating periodic inspection.A system performance degradation model is established.Preventive maintenance probability and corrective renewal probability models are developed to calculate expected warranty costs and system availability.A benefits function,which includes incentives,is constructed to optimize the initial and subsequent inspection intervals and preventive maintenance thresholds,thereby maximizing warranty profit.An improved sparrow search algorithm is developed to optimize the model,with a case study on large steam turbine rotor shafts.The results suggest the optimal PBW strategy involves an initial inspection interval of approximately 20 months,with subsequent intervals of about four months,and a preventive maintenance threshold of approximately 37.39 mm wear.When compared to common cost-minimization-based condition maintenance strategies and PBW strategies that do not differentiate between initial and subsequent inspection intervals,the proposed PBW strategy increases the manufacturer’s profit by 1%and 18%,respectively.Sensitivity analyses provide managerial recommendations for PBW implementation.The PBW strategy proposed in this study significantly increases manufacturers’profits by optimizing inspection intervals and preventive maintenance thresholds,and manufacturers should focus on technological improvement in preventive maintenance and cost control to further enhance earnings. 展开更多
关键词 performance-based warranty gamma process periodic inspection intelligent optimization algorithm
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Towards intelligent design optimization: Progress and challenge of design optimization theories and technologies for plastic forming 认领 引用 被引量：10
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作者 Heng LI Jingchao YANG +4 位作者 Guangyao CHEN Xin LIU Zhao ZHANG Guangjun LI Wenhui LIU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2021年第2期104-123,共20页
Plastic forming is one of enabling and fundamental technologies in advanced manufacturing chains. Design optimization is a critical way to improve the performance of the forming system, exploit the advantages of high ... Plastic forming is one of enabling and fundamental technologies in advanced manufacturing chains. Design optimization is a critical way to improve the performance of the forming system, exploit the advantages of high productivity, high product quality, low production cost and short time to market and develop precise, accurate, green, and intelligent(smart) plastic forming technology. However, plastic forming is quite complicated, relating to multi-physics field coupling,multi-factor influence, multi-defect constraint, and triple nonlinear, etc., and the design optimization for plastic forming involves multi-objective, multi-parameter, multi-constraint, nonlinear,high-dimensionality, non-continuity, time-varying, and uncertainty, etc. Therefore, how to achieve accurate and efficient design optimization of products, equipment, tools/dies, and processing as well as materials characterization has always been the research frontier and focus in the field of engineering and manufacturing. In recent years, with the rapid development of computing science, data science and internet of things(Io T), the theories and technologies of design optimization have attracted more and more attention, and developed rapidly in forming process. Accordingly, this paper first introduced the framework of design optimization for plastic forming. Then, focusing on the key problems of design optimization, such as numerical model and optimization algorithm,this paper summarized the research progress on the development and application of the theories and technologies about design optimization in forming process, including deterministic and uncertain optimization. Moreover, the applicability of various modeling methods and optimization algorithms was elaborated in solving the design optimization problems of plastic forming. Finally, considering the development trends of forming technology, this paper discusses some challenges of design optimization that may need to be solved and faced in forming process. 展开更多
关键词 Advanced manufacturing Design optimization Deterministic optimization(DOP) Intelligent design optimization(IDO) Plastic forming Uncertain optimization(UOP)
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