期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到3,745篇文章
< 1 2 … 188 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
Techno-economic co-optimization of CO2enhanced oil recovery strategies in a tight oil reservoir using coupled improved evolutionary algorithm and machine learning framework 认领 引用
1
作者 Shu-Qin Wen Bing Wei +2 位作者 Jun-Yu You Nan-Jiang Leng William Ampomah 《Petroleum Science》 SCIE EI CAS CSCD 2026年第5期2639-2654,共16页
Massive carbon dioxide(CO2)emissions drive climate change.Injecting CO2into unconventional reservoirsachieves both enhanced oil recovery(EOR)and geological sequestration.However,simultaneously optimizing oil exc... Massive carbon dioxide(CO2)emissions drive climate change.Injecting CO2into unconventional reservoirsachieves both enhanced oil recovery(EOR)and geological sequestration.However,simultaneously optimizing oil exchange ratio,CO2storage,and net present value remainschallenging.This study develops an integrated machine learning(ML)-based framework for multi-objective optimization of CO2-EOR.A high-resolution reservoir simulation was constructed from field data,and Latin hypercube sampling generateddiverse scenarios for proxy training.Mantel's test quantified correlations between input parameters and performance metrics,showing that injection strategy strongly controls net present value,whereas geological properties dominate CO2storage.Three ML models—random forest(RF),support vector regression,and artificial neural networks—were evaluated,with RF selected for its superior performance on small datasets.RF was embedded into an improved non-dominatedsorting genetic algorithm II,enhanced with grey difference degree,crowding distance,and adaptive differential evolution to improve diversity and efficiency.Finally,the technique for order preference by similarity to ideal solution ranked Pareto-optimal solutions through integrating oil productivity,storage,and economics.The proposed framework operationalizes simultaneoushigh-efficiency tight oil recovery and field-scale CO2geological storage,delivering quantitative design rules that embed low-carbon practice into upstream operations and advance the energy sector's greenerand sustainable transition. 展开更多
关键词 CO2enhanced oil recovery Multi-objective optimization Improved non-dominated sorting genetic algorithm II Unconventional oil reservoir
暂未订购 下载PDF
Pigeon-Inspired Optimization Algorithm:Definition,Variants,and Its Applications in Unmanned Aerial Vehicles 认领 引用
2
作者 Yu-Xuan Zhou Kai-Qing Zhou +2 位作者 Wei-Lin Chen Zhou-Hua Liao Di-Wen Kang 《Computers, Materials & Continua》 SCIE EI 2026年第4期186-225,共40页
ThePigeon-InspiredOptimization(PIO)algorithmconstitutes ametaheuristic method derived fromthe homing behaviour of pigeons.Initially formulated for three-dimensional path planning in unmanned aerial vehicles(UAVs),the ... ThePigeon-InspiredOptimization(PIO)algorithmconstitutes ametaheuristic method derived fromthe homing behaviour of pigeons.Initially formulated for three-dimensional path planning in unmanned aerial vehicles(UAVs),the algorithmhas attracted considerable academic and industrial interest owing to its effective balance between exploration and exploitation,coupled with advantages in real-time performance and robustness.Nevertheless,as applications have diversified,limitations in convergence precision and a tendency toward premature convergence have become increasingly evident,highlighting a need for improvement.This reviewsystematically outlines the developmental trajectory of the PIO algorithm,with a particular focus on its core applications in UAV navigation,multi-objective formulations,and a spectrum of variantmodels that have emerged in recent years.It offers a structured analysis of the foundational principles underlying the PIO.It conducts a comparative assessment of various performance-enhanced versions,including hybrid models that integrate mechanisms from other optimization paradigms.Additionally,the strengths andweaknesses of distinct PIOvariants are critically examined frommultiple perspectives,including intrinsic algorithmic characteristics,suitability for specific application scenarios,objective function design,and the rigor of the statistical evaluation methodologies employed in empirical studies.Finally,this paper identifies principal challenges within current PIO research and proposes several prospective research directions.Future work should focus on mitigating premature convergence by refining the two-phase search structure and adjusting the exponential decrease of individual numbers during the landmark operator.Enhancing parameter adaptation strategies,potentially using reinforcement learning for dynamic tuning,and advancing theoretical analyses on convergence and complexity are also critical.Further applications should be explored in constrained path planning,Neural Architecture Search(NAS),and other real-worldmulti-objective problems.For Multi-objective PIO(MPIO),key improvements include controlling the growth of the external archive and designing more effective selection mechanisms to maintain convergence efficiency.These efforts are expected to strengthen both the theoretical foundation and practical versatility of PIO and its variants. 展开更多
关键词 Pigeon-inspired optimization metaheuristic algorithm algorithmvariants swarmintelligence variants UAVs convergence analysis
暂未订购 下载PDF
Improved Gain Shared Knowledge Optimizer Based Reactive Power Optimization for Various Renewable Penetrated Power Grids with Static Var Generator Participation 认领 引用
3
作者 Xuan Ruan HanYan +4 位作者 DonglinHu Min Zhang YingLi DiHai Bo Yang 《Energy Engineering》 EI 2026年第3期23-56,共34页
An optimized volt-ampere reactive(VAR)control framework is proposed for transmission-level power systems to simultaneously mitigate voltage deviations and active-power losses through coordinated control of large-scale... An optimized volt-ampere reactive(VAR)control framework is proposed for transmission-level power systems to simultaneously mitigate voltage deviations and active-power losses through coordinated control of large-scale wind/solar farms with shunt static var generators(SVGs).The model explicitly represents reactive-power regulation characteristics of doubly-fed wind turbines and PV inverters under real-time meteorological conditions,and quantifies SVG high-speed compensation capability,enabling seamless transition from localized VAR management to a globally coordinated strategy.An enhanced adaptive gain-sharing knowledge optimizer(AGSK-SD)integrates simulated annealing and diversity maintenance to autonomously tune voltage-control actions,renewable source reactive-power set-points,and SVG output.The algorithm adaptively modulates knowledge factors and ratios across search phases,performs SA-based fine-grained local exploitation,and periodically re-injects population diversity to prevent premature convergence.Comprehensive tests on IEEE 9-bus and 39-bus systems demonstrate AGSK-SD’s superiority over NSGA-II and MOPSO in hypervolume(HV),inverse generative distance(IGD),and spread metrics while maintaining acceptable computational burden.The method reduces network losses from 2.7191 to 2.15 MW(20.79%reduction)and from 15.1891 to 11.22 MW(26.16%reduction)in the 9-bus and 39-bus systems respectively.Simultaneously,the cumulative voltage-deviation index decreases from 0.0277 to 3.42×10−4 p.u.(98.77%reduction)in the 9-bus system,and from 0.0556 to 0.0107 p.u.(80.76%reduction)in the 39-bus system.These improvements demonstrate significant suppression of line losses and voltage fluctuations.Comparative analysis with traditional heuristic optimization algorithms confirms the superior performance of the proposed approach. 展开更多
关键词 Gained-sharing knowledge improved algorithm adaptive parameter adjustment simulated annealing local search algorithms diversity enhancement mechanisms wind and solar new energy static var generator reactive power optimization
暂未订购 下载PDF
Deep Learning Mixed Hyper-Parameter Optimization Based on Improved Cuckoo Search Algorithm 认领 引用 被引量：1
4
作者 TONG Yu CHEN Rong HU Biling 《Wuhan University Journal of Natural Sciences》 CAS CSCD 2025年第2期195-204,共10页
Deep learning algorithm is an effective data mining method and has been used in many fields to solve practical problems.However,the deep learning algorithms often contain some hyper-parameters which may be continuous,... Deep learning algorithm is an effective data mining method and has been used in many fields to solve practical problems.However,the deep learning algorithms often contain some hyper-parameters which may be continuous,integer,or mixed,and are often given based on experience but largely affect the effectiveness of activity recognition.In order to adapt to different hyper-parameter optimization problems,our improved Cuckoo Search(CS)algorithm is proposed to optimize the mixed hyper-parameters in deep learning algorithm.The algorithm optimizes the hyper-parameters in the deep learning model robustly,and intelligently selects the combination of integer type and continuous hyper-parameters that make the model optimal.Then,the mixed hyper-parameter in Convolutional Neural Network(CNN),Long-Short-Term Memory(LSTM)and CNN-LSTM are optimized based on the methodology on the smart home activity recognition datasets.Results show that the methodology can improve the performance of the deep learning model and whether we are experienced or not,we can get a better deep learning model using our method. 展开更多
关键词 improved Cuckoo Search algorithm mixed hyper-parameter optimization deep learning
暂未订购 下载PDF
Improved coati optimization algorithm through multi-strategy integration:from theoretical design to engineering applications 认领 引用
5
作者 Shuangxi LIU Ruizhe FENG +2 位作者 Yuxin WEI Wei HUANG Binbin YAN 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2025年第12期1197-1210,共14页
Optimization problems are crucial for a wide range of engineering applications,as efficient solutions lead to better performance.This study introduces an improved coati optimization algorithm(ICOA)that overcomes the p... Optimization problems are crucial for a wide range of engineering applications,as efficient solutions lead to better performance.This study introduces an improved coati optimization algorithm(ICOA)that overcomes the primary limitations of the original coati optimization algorithm(COA),notably its insufficient population diversity and propensity to become trapped in local optima.To address these issues,the ICOA integrates three innovative strategies:Latin hypercube sampling(LHS),Lévyflight,and an adaptive local search.LHS is employed to ensure a diverse initial population,thereby laying a foundation for the optimization.Lévy-flight is utilized to facilitate an efficient global search,enhancing the algorithm’s ability to explore the solution space.The adaptive local search is designed to refine solutions,enabling more precise local exploration.Together,these strategies significantly improve the population’s quality and diversity,thereby improving the algorithm’s convergence accuracy and optimization capabilities.The performance of the ICOA is tested against several established algorithms,using 12 benchmark functions.Additionally,the ICOA’s practicality and effectiveness are demonstrated through application to a real-world engineering problem,specifically the design optimization of tension/compression springs.Simulation results show that the ICOA consistently outperforms the other algorithms,providing robust solutions for a wide range of optimization problems. 展开更多
关键词 Improved coati optimization algorithm(ICOA) Latin hypercube sampling(LHS) Lévy-flight Adaptive local search Multi-strategy Engineering applications
暂未订购 下载PDF
Human resource allocation for multiple scientific research projects via improved pigeon-inspired optimization algorithm 认领 引用 被引量：5
6
作者 LIU ChuanBin MA YongHong +1 位作者 YIN Hang YU LeAn 《Science China(Technological Sciences)》 SCIE EI CAS CSCD 2021年第1期139-147,共9页
Aiming at the complex and restrictive characteristics of human resource allocation in multiple scientific university research projects, an improved pigeon-inspired optimization(IPIO) algorithm is proposed wherein loss... Aiming at the complex and restrictive characteristics of human resource allocation in multiple scientific university research projects, an improved pigeon-inspired optimization(IPIO) algorithm is proposed wherein loss minimization and the shortest project delay time are considered as optimization goals. Firstly, mathematical modelling of the problem is carried out, and the multi-objective optimization problem is transformed into a single-objective optimization problem by means of a weighted solution. In the second step, the traditional pigeon-inspired optimization(PIO) algorithm is discretized, and an adaptive parameter strategy is adopted to improve the shortcomings of the algorithm itself. Finally, by comparing the simulation results with the original algorithm and the genetic algorithm in the optimization of human resource allocation in multiple projects, the feasibility and superiority of the proposed algorithm in the optimization of human resource allocation in multi-scientific research projects is verified. 展开更多
关键词 human resource allocation multiple scientific research projects improved pigeon-inspired optimization(IPIO)algorithm parameter adaptation
暂未订购 下载PDF
Energy Efficient Clustering and Sink Mobility Protocol Using Hybrid Golden Jackal and Improved Whale Optimization Algorithm for Improving Network Longevity in WSNs 认领 引用
7
作者 S B Lenin R Sugumar +2 位作者 J S Adeline Johnsana N Tamilarasan R Nathiya 《China Communications》 SCIE EI CSCD 2025年第3期16-35,共20页
Reliable Cluster Head(CH)selectionbased routing protocols are necessary for increasing the packet transmission efficiency with optimal path discovery that never introduces degradation over the transmission reliability... Reliable Cluster Head(CH)selectionbased routing protocols are necessary for increasing the packet transmission efficiency with optimal path discovery that never introduces degradation over the transmission reliability.In this paper,Hybrid Golden Jackal,and Improved Whale Optimization Algorithm(HGJIWOA)is proposed as an effective and optimal routing protocol that guarantees efficient routing of data packets in the established between the CHs and the movable sink.This HGJIWOA included the phases of Dynamic Lens-Imaging Learning Strategy and Novel Update Rules for determining the reliable route essential for data packets broadcasting attained through fitness measure estimation-based CH selection.The process of CH selection achieved using Golden Jackal Optimization Algorithm(GJOA)completely depends on the factors of maintainability,consistency,trust,delay,and energy.The adopted GJOA algorithm play a dominant role in determining the optimal path of routing depending on the parameter of reduced delay and minimal distance.It further utilized Improved Whale Optimisation Algorithm(IWOA)for forwarding the data from chosen CHs to the BS via optimized route depending on the parameters of energy and distance.It also included a reliable route maintenance process that aids in deciding the selected route through which data need to be transmitted or re-routed.The simulation outcomes of the proposed HGJIWOA mechanism with different sensor nodes confirmed an improved mean throughput of 18.21%,sustained residual energy of 19.64%with minimized end-to-end delay of 21.82%,better than the competitive CH selection approaches. 展开更多
关键词 Cluster Heads(CHs) Golden Jackal Optimization Algorithm(GJOA) Improved Whale Optimization Algorithm(IWOA) unequal clustering
暂未订购 下载PDF
Improved Guide-Weight method for multi-material topology optimization under inertial loads based on the alternating active-phase algorithm 认领 引用
8
作者 Zihao Meng Yiru Ren 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2025年第8期138-148,共11页
The application of multi-material topology optimization affords greater design flexibility compared to traditional single-material methods.However,density-based topology optimization methods encounter three unique cha... The application of multi-material topology optimization affords greater design flexibility compared to traditional single-material methods.However,density-based topology optimization methods encounter three unique challenges when inertial loads become dominant:non-monotonous behavior of the objective function,possible unconstrained characterization of the optimal solution,and parasitic effects.Herein,an improved Guide-Weight approach is introduced,which effectively addresses the structural topology optimization problem when subjected to inertial loads.Smooth and fast convergence of the compliance is achieved by the approach,while also maintaining the effectiveness of the volume constraints.The rational approximation of material properties model and smooth design are utilized to guarantee clear boundaries of the final structure,facilitating its seamless integration into manufacturing processes.The framework provided by the alternating active-phase algorithm is employed to decompose the multi-material topological problem under inertial loading into a set of sub-problems.The optimization of multi-material under inertial loads is accomplished through the effective resolution of these sub-problems using the improved Guide-Weight method.The effectiveness of the proposed approach is demonstrated through numerical examples involving two-phase and multi-phase materials. 展开更多
关键词 Topology optimization Improved Guide-Weight method Alternating active-phase algorithm Inertial loads Multi-material
暂未订购 下载PDF
Research on the Optimal Scheduling Model of Energy Storage Plant Based on Edge Computing and Improved Whale Optimization Algorithm 认领 引用
9
作者 Zhaoyu Zeng Fuyin Ni 《Energy Engineering》 EI 2025年第3期1153-1174,共22页
Energy storage power plants are critical in balancing power supply and demand.However,the scheduling of these plants faces significant challenges,including high network transmission costs and inefficient inter-device ... Energy storage power plants are critical in balancing power supply and demand.However,the scheduling of these plants faces significant challenges,including high network transmission costs and inefficient inter-device energy utilization.To tackle these challenges,this study proposes an optimal scheduling model for energy storage power plants based on edge computing and the improved whale optimization algorithm(IWOA).The proposed model designs an edge computing framework,transferring a large share of data processing and storage tasks to the network edge.This architecture effectively reduces transmission costs by minimizing data travel time.In addition,the model considers demand response strategies and builds an objective function based on the minimization of the sum of electricity purchase cost and operation cost.The IWOA enhances the optimization process by utilizing adaptive weight adjustments and an optimal neighborhood perturbation strategy,preventing the algorithm from converging to suboptimal solutions.Experimental results demonstrate that the proposed scheduling model maximizes the flexibility of the energy storage plant,facilitating efficient charging and discharging.It successfully achieves peak shaving and valley filling for both electrical and heat loads,promoting the effective utilization of renewable energy sources.The edge-computing framework significantly reduces transmission delays between energy devices.Furthermore,IWOA outperforms traditional algorithms in optimizing the objective function. 展开更多
关键词 Energy storage plant edge computing optimal energy scheduling improved whale optimization algorithm
暂未订购 下载PDF
An Improved Pigeon-Inspired Optimization for Multi-focus Noisy Image Fusion 认领 引用 被引量：1
10
作者 Yingda Lyu Yunqi Zhang Haipeng Chen 《Journal of Bionic Engineering》 SCIE EI CSCD 2021年第6期1452-1462,共11页
Image fusion technology is the basis of computer vision task,but information is easily affected by noise during transmission.In this paper,an Improved Pigeon-Inspired Optimization(IPIO)is proposed,and used for multi-f... Image fusion technology is the basis of computer vision task,but information is easily affected by noise during transmission.In this paper,an Improved Pigeon-Inspired Optimization(IPIO)is proposed,and used for multi-focus noisy image fusion by combining with the boundary handling of the convolutional sparse representation.By two-scale image decomposition,the input image is decomposed into base layer and detail layer.For the base layer,IPIO algorithm is used to obtain the optimized weights for fusion,whose value range is gained by fusing the edge information.Besides,the global information entropy is used as the fitness index of the IPIO,which has high efficiency especially for discrete optimization problems.For the detail layer,the fusion of its coefficients is completed by performing boundary processing when solving the convolution sparse representation in the frequency domain.The sum of the above base and detail layers is as the final fused image.Experimental results show that the proposed algorithm has a better fusion effect compared with the recent algorithms. 展开更多
关键词 Improved pigeon-inspired optimization Convolutional sparse representation Noisy image fusion Bionic algorithm
Optimization design of launch window for large-scale constellation using improved genetic algorithm 认领 引用
11
作者 LIU Yue HOU Xiangzhen +3 位作者 CAI Xi LI Minghu CHANG Xinya WANG Miao 《先进小卫星技术（中英文）》 2025年第4期23-32,共10页
The research on optimization methods for constellation launch deployment strategies focused on the consideration of mission interval time constraints at the launch site.Firstly,a dynamic modeling of the constellation ... The research on optimization methods for constellation launch deployment strategies focused on the consideration of mission interval time constraints at the launch site.Firstly,a dynamic modeling of the constellation deployment process was established,and the relationship between the deployment window and the phase difference of the orbit insertion point,as well as the cost of phase adjustment after orbit insertion,was derived.Then,the combination of the constellation deployment position sequence was treated as a parameter,together with the sequence of satellite deployment intervals,as optimization variables,simplifying a highdimensional search problem within a wide range of dates to a finite-dimensional integer programming problem.An improved genetic algorithm with local search on deployment dates was introduced to optimize the launch deployment strategy.With the new description of the optimization variables,the total number of elements in the solution space was reduced by N orders of magnitude.Numerical simulation confirms that the proposed optimization method accelerates the convergence speed from hours to minutes. 展开更多
关键词 deployment strategy optimization launching schedule constraints improved genetic algorithm large-scale constellation
暂未订购 下载PDF
Efficient Adaptive Collision-Free Path Planning Algorithm for Indoor Mobile Robots:Optimization and Application 认领 引用
12
作者 Xiangyang Lu Yandan Wang +1 位作者 Guangyi Zang Junqi Wang 《Journal of Beijing Institute of Technology》 EI CAS 2026年第2期205-217,共13页
An adaptive path planning algorithm was proposed,which improves upon traditional A*by integrating an improved A*algorithm with the Dynamic Window Approach(DWA).This addresses the problems of slow search speed,unsmooth... An adaptive path planning algorithm was proposed,which improves upon traditional A*by integrating an improved A*algorithm with the Dynamic Window Approach(DWA).This addresses the problems of slow search speed,unsmooth paths,and poor dynamic obstacle avoidance capability.Through an“8+5”neighborhood screening,a 16-neighborhood evaluation function,and a second-order then third-order Bézier curve optimization process,a Jetson Nano+ROS(Robot Operating System)is deployed to meet the requirements of efficient and safe navigation for fire inspection robots in complex environments.The results show that,compared with the original algorithm,the proposed algorithm reduces the average number of traversed nodes by 49.23%,the number of turns in the optimized path has decreased by approximately 28.82%,decreases curvature by 66.6%,and eliminates path tangency with obstacles.This also supports real-time obstacle avoidance with integration DWA,and outperforms traditional methods. 展开更多
关键词 adaptive path planning improved A*algorithm Bézier curve optimization fire inspection robot Jetson Nano+ROS system
暂未订购 下载PDF
An Improved Whale Algorithm and Its Application in Truss Optimization 认领 引用 被引量：9
13
作者 Fengguo Jiang Lutong Wang Lili Bai 《Journal of Bionic Engineering》 SCIE EI CSCD 2021年第3期721-732,共12页
The current Whale Optimization Algorithm(WOA)has several drawbacks,such as slow convergence,low solution accuracy and easy to fall into the local optimal solution.To overcome these drawbacks,an improved Whale Optimiza... The current Whale Optimization Algorithm(WOA)has several drawbacks,such as slow convergence,low solution accuracy and easy to fall into the local optimal solution.To overcome these drawbacks,an improved Whale Optimization Algorithm(IWOA)is proposed in this study.IWOA can enhance the global search capability by two measures.First,the crossover and mutation operations in Differential Evolutionary algorithm(DE)are combined with the whale optimization algorithm.Second,the cloud adaptive inertia weight is introduced in the position update phase of WOA to divide the population into two subgroups,so as to balance the global search ability and local development ability.ANSYS and Matlab are used to establish the structure model.To demonstrate the application of the IWOA,truss structural optimizations on 52-bar plane truss and 25-bar space truss were performed,and the results were are compared with that obtained by other optimization algorithm.It is verified that,compared with WOA,the IWOA has higher efficiency,fast convergence speed,better solution accuracy and stability.So IWOA can be used in the optimization design of large truss structures. 展开更多
关键词 improve whale optimization algorithm differential evolutionary algorithm cloud theory simulating optimization bionic algorithm
Optimization of jamming formation of USV offboard active decoy clusters based on an improved PSO algorithm 认领 引用 被引量：4
14
作者 Zhaodong Wu Yasong Luo Shengliang Hu 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2024年第2期529-540,共12页
Offboard active decoys(OADs)can effectively jam monopulse radars.However,for missiles approaching from a particular direction and distance,the OAD should be placed at a specific location,posing high requirements for t... Offboard active decoys(OADs)can effectively jam monopulse radars.However,for missiles approaching from a particular direction and distance,the OAD should be placed at a specific location,posing high requirements for timing and deployment.To improve the response speed and jamming effect,a cluster of OADs based on an unmanned surface vehicle(USV)is proposed.The formation of the cluster determines the effectiveness of jamming.First,based on the mechanism of OAD jamming,critical conditions are identified,and a method for assessing the jamming effect is proposed.Then,for the optimization of the cluster formation,a mathematical model is built,and a multi-tribe adaptive particle swarm optimization algorithm based on mutation strategy and Metropolis criterion(3M-APSO)is designed.Finally,the formation optimization problem is solved and analyzed using the 3M-APSO algorithm under specific scenarios.The results show that the improved algorithm has a faster convergence rate and superior performance as compared to the standard Adaptive-PSO algorithm.Compared with a single OAD,the optimal formation of USV-OAD cluster effectively fills the blind area and maximizes the use of jamming resources. 展开更多
关键词 Electronic countermeasure Offboard active decoy USV cluster Jamming formation optimization Improved PSO algorithm
暂未订购 下载PDF
Estimation of state of health based on charging characteristics and back-propagation neural networks with improved atom search optimization algorithm 认领 引用 被引量：4
15
作者 Yu Zhang Yuhang Zhang Tiezhou Wu 《Global Energy Interconnection》 EI CSCD 2023年第2期228-237,共10页
With the rapid development of new energy technologies, lithium batteries are widely used in the field of energy storage systems and electric vehicles. The accurate prediction for the state of health(SOH) has an import... With the rapid development of new energy technologies, lithium batteries are widely used in the field of energy storage systems and electric vehicles. The accurate prediction for the state of health(SOH) has an important role in maintaining a safe and stable operation of lithium-ion batteries. To address the problems of uncertain battery discharge conditions and low SOH estimation accuracy in practical applications, this paper proposes a SOH estimation method based on constant-current battery charging section characteristics with a back-propagation neural network with an improved atom search optimization algorithm. A temperature characteristic, equal-time temperature variation(Dt_DT), is proposed by analyzing the temperature data of the battery charging section with the incremental capacity(IC) characteristics obtained from an IC analysis as an input to the data-driven prediction model. Testing and analysis of the proposed prediction model are carried out using publicly available datasets. Experimental results show that the maximum error of SOH estimation results for the proposed method in this paper is below 1.5%. 展开更多
关键词 State of health Lithium-ion battery Dt_DT Improved atom search optimization algorithm
暂未订购 下载PDF
Solving Job-Shop Scheduling Problem Based on Improved Adaptive Particle Swarm Optimization Algorithm 认领 引用 被引量：4
16
作者 顾文斌 唐敦兵 郑堃 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2014年第5期559-567,共9页
An improved adaptive particle swarm optimization(IAPSO)algorithm is presented for solving the minimum makespan problem of job shop scheduling problem(JSP).Inspired by hormone modulation mechanism,an adaptive hormonal ... An improved adaptive particle swarm optimization(IAPSO)algorithm is presented for solving the minimum makespan problem of job shop scheduling problem(JSP).Inspired by hormone modulation mechanism,an adaptive hormonal factor(HF),composed of an adaptive local hormonal factor(H l)and an adaptive global hormonal factor(H g),is devised to strengthen the information connection between particles.Using HF,each particle of the swarm can adjust its position self-adaptively to avoid premature phenomena and reach better solution.The computational results validate the effectiveness and stability of the proposed IAPSO,which can not only find optimal or close-to-optimal solutions but also obtain both better and more stability results than the existing particle swarm optimization(PSO)algorithms. 展开更多
关键词 job-shop scheduling problem(JSP) hormone modulation mechanism improved adaptive particle swarm optimization(IAPSO) algorithm minimum makespan
暂未订购 下载PDF
Improved Teaching-Learning-Based Optimization Algorithm for Modeling NOX Emissions of a Boiler 认领 引用 被引量：1
17
作者 Xia Li Peifeng Niu +1 位作者 Jianping Liu Qing Liu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2018年第10期29-57,共29页
An improved teaching-learning-based optimization(I-TLBO)algorithm is proposed to adjust the parameters of extreme learning machine with parallel layer perception(PELM),and a well-generalized I-TLBO-PELM model is obtai... An improved teaching-learning-based optimization(I-TLBO)algorithm is proposed to adjust the parameters of extreme learning machine with parallel layer perception(PELM),and a well-generalized I-TLBO-PELM model is obtained to build the model of NOX emissions of a boiler.In the I-TLBO algorithm,there are four major highlights.Firstly,a quantum initialized population by using the qubits on Bloch sphere replaces a randomly initialized population.Secondly,two kinds of angles in Bloch sphere are generated by using cube chaos mapping.Thirdly,an adaptive control parameter is added into the teacher phase to speed up the convergent speed.And then,according to actual teaching-learning phenomenon of a classroom,students learn some knowledge not only by their teacher and classmates,but also by themselves.Therefore,a self-study strategy by using Gauss mutation is introduced after the learning phase to improve the exploration ability.Finally,we test the performance of the I-TLBO-PELM model.The experiment results show that the proposed model has better regression precision and generalization ability than eight other models. 展开更多
关键词 Bloch sphere qubits self-learning improved teaching-learning-based optimization(I-TLBO)algorithm
暂未订购 下载PDF
Optimal Configuration of Fault Location Measurement Points in DC Distribution Networks Based on Improved Particle Swarm Optimization Algorithm 认领 引用 被引量：1
18
作者 Huanan Yu Hangyu Li +1 位作者 He Wang Shiqiang Li 《Energy Engineering》 EI 2024年第6期1535-1555,共21页
The escalating deployment of distributed power sources and random loads in DC distribution networks hasamplified the potential consequences of faults if left uncontrolled. To expedite the process of achieving an optim... The escalating deployment of distributed power sources and random loads in DC distribution networks hasamplified the potential consequences of faults if left uncontrolled. To expedite the process of achieving an optimalconfiguration of measurement points, this paper presents an optimal configuration scheme for fault locationmeasurement points in DC distribution networks based on an improved particle swarm optimization algorithm.Initially, a measurement point distribution optimization model is formulated, leveraging compressive sensing.The model aims to achieve the minimum number of measurement points while attaining the best compressivesensing reconstruction effect. It incorporates constraints from the compressive sensing algorithm and networkwide viewability. Subsequently, the traditional particle swarm algorithm is enhanced by utilizing the Haltonsequence for population initialization, generating uniformly distributed individuals. This enhancement reducesindividual search blindness and overlap probability, thereby promoting population diversity. Furthermore, anadaptive t-distribution perturbation strategy is introduced during the particle update process to enhance the globalsearch capability and search speed. The established model for the optimal configuration of measurement points issolved, and the results demonstrate the efficacy and practicality of the proposed method. The optimal configurationreduces the number of measurement points, enhances localization accuracy, and improves the convergence speedof the algorithm. These findings validate the effectiveness and utility of the proposed approach. 展开更多
关键词 Optimal allocation improved particle swarm algorithm fault location compressed sensing DC distribution network
暂未订购 下载PDF
Buffer allocation method of serial production lines based on improved ant colony optimization algorithm 认领 引用 被引量：2
19
作者 周炳海 Yu Jiadi 《High Technology Letters》 EI CAS 2016年第2期113-119,共7页
Buffer influences the performance of production lines greatly.To solve the buffer allocation problem(BAP) in serial production lines with unreliable machines effectively,an optimization method is proposed based on an ... Buffer influences the performance of production lines greatly.To solve the buffer allocation problem(BAP) in serial production lines with unreliable machines effectively,an optimization method is proposed based on an improved ant colony optimization(IACO) algorithm.Firstly,a problem domain describing buffer allocation is structured.Then a mathematical programming model is established with an objective of maximizing throughput rate of the production line.On the basis of the descriptions mentioned above,combining with a two-opt strategy and an acceptance probability rule,an IACO algorithm is built to solve the BAP.Finally,the simulation experiments are designed to evaluate the proposed algorithm.The results indicate that the IACO algorithm is valid and practical. 展开更多
关键词 buffer allocation improved ant colony optimization （IACO） algorithm serial pro-duction line throughput rate
暂未订购 下载PDF
Research on the Optimization Approach for Cargo Oil Tank Design Based on the Improved Particle Swarm Optimization Algorithm 认领 引用 被引量：1
20
作者 姜文英 林焰 +1 位作者 陈明 于雁云 《Journal of Shanghai Jiaotong university(Science)》 EI 2015年第5期565-570,共6页
Based on the improved particle swarm optimization(PSO) algorithm,an optimization approach for the cargo oil tank design(COTD) is presented in this paper.The purpose is to design an optimal overall dimension of the car... Based on the improved particle swarm optimization(PSO) algorithm,an optimization approach for the cargo oil tank design(COTD) is presented in this paper.The purpose is to design an optimal overall dimension of the cargo oil tank(COT) under various kinds of constraints in the preliminary design stage.A non-linear programming model is built to simulate the optimization design,in which the requirements and rules for COTD are used as the constraints.Considering the distance between the inner shell and hull,a fuzzy constraint is used to express the feasibility degree of the double-hull configuration.In terms of the characteristic of COTD,the PSO algorithm is improved to solve this problem.A bivariate extremum strategy is presented to deal with the fuzzy constraint,by which the maximum and minimum cargo capacities are obtained simultaneously.Finally,the simulation demonstrates the feasibility and effectiveness of the proposed approach. 展开更多
关键词 cargo oil tank optimization design nonlinear programming improved particle swarm optimization（PSO）algorithm fuzzy constraint construction feasibility degree
暂未订购 下载PDF
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 2 … 188 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈