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66篇文章
< 1 2 … 4 >
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：
PROMPTx-PE:Adaptive Optimization of Prompt Engineering Strategies for Accuracy and Robustness in Large Language Models 认领 引用
1
作者 Talha Farooq Khan Fahad Ali +2 位作者 Majid Hussain Lal Khan Hsien-Tsung Chang 《Computers, Materials & Continua》 SCIE EI 2026年第5期685-715,共31页
The outstanding growth in the applications of large language models(LLMs)demonstrates the significance of adaptive and efficient prompt engineering tactics.The existing methods may not be variable,vigorous and streaml... The outstanding growth in the applications of large language models(LLMs)demonstrates the significance of adaptive and efficient prompt engineering tactics.The existing methods may not be variable,vigorous and streamlined in different domains.The offered study introduces an immediate optimization outline,named PROMPTx-PE,that is going to yield a greater level of precision and strength when it comes to the assignments that are premised on LLM.The proposed systemfeatures a timely selection schemewhich is informed by reinforcement learning,a contextual layer and a dynamic weighting module which is regulated by Lyapunov-based stability guidelines.The PROMPTx-PE dynamically varies the exploration and exploitation of the prompt space,depending on real-time feedback and multi-objective reward development.Extensive testing on both benchmark(GLUE,SuperGLUE)and domain-specific data(Healthcare-QA and Industrial-NER)demonstrates a large best performance to be 89.4%and a strong robustness disconnect with under 3%computation expense.The results confirm the effectiveness,consistency,and scalability of PROMPTx-PE as a platform of adaptive prompt engineering based on recent uses of LLMs. 展开更多
关键词 Prompt engineering large language models adaptive optimization robustness multi-objective optimization reinforcement learning natural language processing
暂未订购 下载PDF
DRAGON-MINE:Deep Reinforcement Adaptive Gradient Optimization Network for Mining Rare Events in Healthcare 认领 引用
2
作者 Mohammed Abdullah Alsuwaiket 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第3期967-996,共30页
The healthcare field is fraught with challenges associated with severe class imbalance,wherein such critical conditions like sepsis,cardiac arrest,and drug adverse reactions are rare but have dire clinical consequence... The healthcare field is fraught with challenges associated with severe class imbalance,wherein such critical conditions like sepsis,cardiac arrest,and drug adverse reactions are rare but have dire clinical consequences.This paper presents a new framework,Deep Reinforcement Adaptive Gradient Optimization Network to Mining Rare Events(DRAGON-MINE),to demonstrate how deep reinforcement learning can be used synergistically with adaptive gradient optimization and address the inherent weaknesses of current methods in the prediction of rare health events.The suggested architecture uses a dual-pathway consisting of a reinforcement learning agent to dynamically reweigh samples and an adaptive gradient optimizer to follow novel learning rates.With extensive experiments on the MIMIC-IV and eICU-CRD datasets,DRAGON-MINE consistently outperforms recent state-of-the-art methods for sepsis,cardiac arrest,and adverse drug reaction prediction,achieving AUROC values of 92.3%and 91.6%for sepsis prediction on MIMIC-IV and eICU-CRD,respectively,while consistently outperforming Transformer-,CNN-RNN-,and Fed-Ensemble-based methods across all evaluated tasks and datasets,with particularly strong gains observed in precision-recall performance under severe class imbalance.With its high sensitivity(88.4%)and specificity(90.2%),DRAGON-MINE enables reliable early warning of rare clinical events in critical care settings while minimizing false alarms,supporting safer clinical decision support systems,and demonstrating strong potential for scalable deployment across multi-institutional intensive care environments through federated learning. 展开更多
关键词 Deep reinforcement learning rare event prediction class imbalance healthcare AI adaptive gradient optimization sepsis detection federated learning
暂未订购 下载PDF
Optimization Strategy for Radar Signal Anti-Jamming in UAV Emergency Delivery 认领 引用
3
作者 Fan Yang 《Journal of Electronic Research and Application》 2026年第2期141-151,共11页
Drones in emergency delivery operations face complex electromagnetic interference,including active jamming such as suppression and deception types,and passive jamming like chaff,which seriously threatens the reliabili... Drones in emergency delivery operations face complex electromagnetic interference,including active jamming such as suppression and deception types,and passive jamming like chaff,which seriously threatens the reliability of radar detection.Constrained by platform payload and computational power,traditional anti-jamming techniques are difficult to apply directly.This study proposes a set of optimization strategies centered on lightweight design and adaptability,including interference-aware waveform optimization,joint spatiotemporal processing,jamming evasion methods,and lightweight algorithm design.Through theoretical analysis and numerical simulation,the strategy achieves a Signal-to-Interference-plus-Noise Ratio(SINR)improvement of up to 22 dB under compound interference,maintains a stable target detection probability above 90%,and reduces computational complexity to only 30-45%of that of traditional methods.This effectively balances performance with resource constraints,thereby enhancing the reliability of delivery missions. 展开更多
关键词 Radar signal anti-jamming Adaptive waveform optimization Lightweight algorithm Compound interference Signal-to-interference-plus-noise ratio
暂未订购 下载PDF
Developed Time-OptimalModel Predictive Static Programming Method with Fish Swarm Optimization for Near-Space Vehicle 认领 引用
4
作者 Yuanzhuo Wang Honghua Dai 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第5期1463-1484,共22页
To establish the optimal reference trajectory for a near-space vehicle under free terminal time,a time-optimal model predictive static programming method is proposed with adaptive fish swarm optimization.First,the mod... To establish the optimal reference trajectory for a near-space vehicle under free terminal time,a time-optimal model predictive static programming method is proposed with adaptive fish swarm optimization.First,the model predictive static programming method is developed by incorporating neighboring terms and trust region,enabling rapid generation of precise optimal solutions.Next,an adaptive fish swarm optimization technique is employed to identify a sub-optimal solution,while a momentum gradient descent method with learning rate decay ensures the convergence to the global optimal solution.To validate the feasibility and accuracy of the proposed method,a near-space vehicle example is analyzed and simulated during its glide phase.The simulation results demonstrate that the proposed method aligns with theoretical derivations and outperforms existing methods in terms of convergence speed and accuracy.Therefore,the proposed method offers significant practical value for solving the fast trajectory optimization problem in near-space vehicle applications. 展开更多
关键词 Near-space vehicle model predictive static programming neighboring term and trust region optimal control adaptive fish swarm optimization
暂未订购 下载PDF
Intelligent Safe Optimal Control Towards Koopman Operator-Driven Nonlinear Systems With Asymmetric State and Input Constraints 认领 引用
5
作者 Yalu Su Ding Wang +3 位作者 Mingming Zhao Dan Xiong Yiyong Huang Wei Han 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第5期1135-1150,共16页
For unknown nonlinear systems subject to asymmetric state and input constraints simultaneously,this article establishes a safe value iteration paradigm to learn an optimal control policy in a data-based manner.Initial... For unknown nonlinear systems subject to asymmetric state and input constraints simultaneously,this article establishes a safe value iteration paradigm to learn an optimal control policy in a data-based manner.Initially,the Koopman operator,instead of the black-box neural network,is applied to extract the inherent dynamics of the controlled systems from the measured data,thereby allowing for explicit analysis of the prediction error.To tackle the issue posed by state and input constraints,a crafted control barrier function is seamlessly incorporated into the canonical utility function,which retains the property of positive definiteness for the asymmetric case.Moreover,the value iteration algorithm with regard to the augmented utility function is adopted to attain a safe optimal controller,where the actor and critic networks are leveraged to approximate the control input and associated value function,respectively.The monotonicity,safety,and stability of the raised algorithm are further verified rigorously.Via performing three experiments on the linear system,the nonlinear system,and the manipulator plant,comparative results are obtained to substantiate the superiority and efficacy of the developed approach in achieving optimal performance and safe guarantee. 展开更多
关键词 Adaptive optimal control constrained value iteration control barrier function discrete-time nonlinear systems reinforcement learning Koopman operator
暂未订购 下载PDF
ADAPT:A Model-Free Adaptive Optimal Control for Continuum Robots 认领 引用
6
作者 Haiyang Fang Sishen Yuan +2 位作者 Hongliang Ren Shuping He Shing Shin Cheng 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第1期205-217,共13页
Realizing optimal control performance for continuum robots(CRs) poses huge challenges on traditional modelbased optimal control approaches due to their high degrees of freedom,complex nonlinear dynamics and soft conti... Realizing optimal control performance for continuum robots(CRs) poses huge challenges on traditional modelbased optimal control approaches due to their high degrees of freedom,complex nonlinear dynamics and soft continuum morphologies which are difficult to explicitly model.This paper proposes a model-free adaptive optimal control algorithm(ADAPT)for CRs.In our strategy,we consider CRs as a class of nonlinear continuous-time dynamical systems in the state space,wherein the position of the end-effector is considered as the state and the input torque is mapped as the control input.Then,the optimized Hamilton-Jacobi-Bellman(HJB) equation is derived by optimal control principles,and subsequently solved by the proposed ADAPT algorithm without requiring knowledge of the original system dynamics.Under some mild assumptions,the global stability and convergence of the closed-loop control approach are guaranteed.Several simulation experiments are conducted on a magnetic CR(MCR) to demonstrate the practicality and effectiveness of the ADAPT algorithm. 展开更多
关键词 Adaptive optimal control continuum robots(CRs) Hamilton-Jacobi-Bellman(HJB)equation model-free approach
暂未订购 下载PDF
Adaptive optimization of agile organization of command and control resource 认领 引用 被引量：8
7
作者 Yang Chunhui Liu Junxian +1 位作者 Chen Honghui Luo Xueshan 《Journal of Systems Engineering and Electronics》 SCIE EI 2009年第3期558-564,共7页
Adaptive optimization is one of the means that agile organization of command and control resource （AOC2R） adapts for the dynamic battlefield environment. A math model of the adaptive optimization of AOC2R is put for... Adaptive optimization is one of the means that agile organization of command and control resource （AOC2R） adapts for the dynamic battlefield environment. A math model of the adaptive optimization of AOC2R is put forward by analyzing the interrelating concept and research. The model takes the adaptive process as a multi-stage decision making problem. The 2-phases method is presented to calculate the model, which obtains the related parameters by running the colored Petri net （CPN） model of AOC2R and then searches for the result by ant colony optimization （ACO） algorithm integrated with genetic optimization techniques. The simulation results demonstrate that the proposed algorithm greatly improves the performance of AOC2R. 展开更多
关键词 command and control organization adaptive optimization of organization dynamic-window-search ant colony optimization 3-phase organizational design.
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
8
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
Deep learning CNN-APSO-LSSVM hybrid fusion model for feature optimization and gas-bearing prediction 认领 引用 被引量：1
9
作者 Jiu-Qiang Yang Nian-Tian Lin +3 位作者 Kai Zhang Yan Cui Chao Fu Dong Zhang 《Petroleum Science》 SCIE EI CAS CSCD 2024年第4期2329-2344,共16页
Conventional machine learning(CML)methods have been successfully applied for gas reservoir prediction.Their prediction accuracy largely depends on the quality of the sample data;therefore,feature optimization of the i... Conventional machine learning(CML)methods have been successfully applied for gas reservoir prediction.Their prediction accuracy largely depends on the quality of the sample data;therefore,feature optimization of the input samples is particularly important.Commonly used feature optimization methods increase the interpretability of gas reservoirs;however,their steps are cumbersome,and the selected features cannot sufficiently guide CML models to mine the intrinsic features of sample data efficiently.In contrast to CML methods,deep learning(DL)methods can directly extract the important features of targets from raw data.Therefore,this study proposes a feature optimization and gas-bearing prediction method based on a hybrid fusion model that combines a convolutional neural network(CNN)and an adaptive particle swarm optimization-least squares support vector machine(APSO-LSSVM).This model adopts an end-to-end algorithm structure to directly extract features from sensitive multicomponent seismic attributes,considerably simplifying the feature optimization.A CNN was used for feature optimization to highlight sensitive gas reservoir information.APSO-LSSVM was used to fully learn the relationship between the features extracted by the CNN to obtain the prediction results.The constructed hybrid fusion model improves gas-bearing prediction accuracy through two processes of feature optimization and intelligent prediction,giving full play to the advantages of DL and CML methods.The prediction results obtained are better than those of a single CNN model or APSO-LSSVM model.In the feature optimization process of multicomponent seismic attribute data,CNN has demonstrated better gas reservoir feature extraction capabilities than commonly used attribute optimization methods.In the prediction process,the APSO-LSSVM model can learn the gas reservoir characteristics better than the LSSVM model and has a higher prediction accuracy.The constructed CNN-APSO-LSSVM model had lower errors and a better fit on the test dataset than the other individual models.This method proves the effectiveness of DL technology for the feature extraction of gas reservoirs and provides a feasible way to combine DL and CML technologies to predict gas reservoirs. 展开更多
关键词 Multicomponent seismic data Deep learning Adaptive particle swarm optimization Convolutional neural network Least squares support vector machine Feature optimization Gas-bearing distribution prediction
暂未订购 下载PDF
An adaptive reanalysis method for genetic algorithm with application to fast truss optimization 认领 引用 被引量：3
10
作者 Tao Xu Wenjie Zuo +2 位作者 Tianshuang Xu Guangcai Song Ruichuan Li 《Acta Mechanica Sinica》 SCIE EI CAS 2010年第2期225-234,共10页
Although the genetic algorithm （GA） for structural optimization is very robust, it is very computationally intensive and hence slower than optimality criteria and mathematical programming methods. To speed up the de... Although the genetic algorithm （GA） for structural optimization is very robust, it is very computationally intensive and hence slower than optimality criteria and mathematical programming methods. To speed up the design process, the authors present an adaptive reanalysis method for GA and its applications in the optimal design of trusses. This reanalysis technique is primarily derived from the Kirsch＇s combined approximations method. An iteration scheme is adopted to adaptively determine the number of basis vectors at every generation. In order to illustrate this method, three classical examples of optimal truss design are used to validate the proposed reanalysis-based design procedure. The presented numerical results demonstrate that the adaptive reanalysis technique affects very slightly the accuracy of the optimal solutions and does accelerate the design process, especially for large-scale structures. 展开更多
关键词 Truss structure Adaptive reanalysis ·Genetic algorithm ·Fast optimization
暂未订购 下载PDF
A New Clustering Algorithm Using Adaptive Discrete Particle Swarm Optimization in Wireless Sensor Network 认领 引用 被引量：4
11
作者 余朝龙 郭文忠 《Journal of Donghua University(English Edition)》 EI CAS 2012年第1期19-22,共4页
Wireless sensor networks (WSNs) are mainly characterized by their limited and non-replenishable energy supply. Hence, the energy efficiency of the infrastructure greatly affects the network lifetime. Clustering is one... Wireless sensor networks (WSNs) are mainly characterized by their limited and non-replenishable energy supply. Hence, the energy efficiency of the infrastructure greatly affects the network lifetime. Clustering is one of the methods that can expand the lifespan of the whole network by grouping the sensor nodes according to some criteria and choosing the appropriate cluster heads(CHs). The balanced load of the CHs has an important effect on the energy consumption balancing and lifespan of the whole network. Therefore, a new CHs election method is proposed using an adaptive discrete particle swarm optimization (ADPSO) algorithm with a fitness value function considering the load balancing and energy consumption. Simulation results not only demonstrate that the proposed algorithm can have better performance in load balancing than low-energy adaptive clustering hierarchy (LEACH), hybrid energy-efficient distributed clustering (HEED), and dynamic clustering algorithm with balanced load (DCBL), but also imply that the proposed algorithm can extend the network lifetime more. 展开更多
关键词 load balancing energy consumption balancing cluster head(CH) adaptive discrete particle swarm optimization (ADPSO)
暂未订购 下载PDF
Adaptive Maxwell’s Equations Derived Optimization and Its Application in Antenna Array Synthesis 认领 引用 被引量：2
12
作者 Donglin Su Lilin Li +1 位作者 Shunchuan Yang Fei Wang 《China Communications》 SCIE EI CSCD 2021年第5期263-272,共10页
In this paper,a self-adaptive method for the Maxwell’s Equations Derived Optimization(MEDO)is proposed.It is implemented by applying the Sequential Model-Based Optimization(SMBO)algorithm to the iterations of the MED... In this paper,a self-adaptive method for the Maxwell’s Equations Derived Optimization(MEDO)is proposed.It is implemented by applying the Sequential Model-Based Optimization(SMBO)algorithm to the iterations of the MEDO,and achieves the automatic adjustment of the parameters.The proposed method is named as adaptive Maxwell’s equations derived optimization(AMEDO).In order to evaluate the performance of AMEDO,eight benchmarks are used and the results are compared with the original MEDO method.The results show that AMEDO can greatly reduce the workload of manual adjustment of parameters,and at the same time can keep the accuracy and stability.Moreover,the convergence of the optimization can be accelerated due to the dynamical adjustment of the parameters.In the end,the proposed AMEDO is applied to the side lobe level suppression and array failure correction of a linear antenna array,and shows great potential in antenna array synthesis. 展开更多
关键词 electromagnetic compatibility Maxwell’s equations derived optimization adaptive Maxwell’s equations derived optimization sequential modelbased optimization antenna array synthesis
暂未订购 下载PDF
Covid-19 CT Lung Image Segmentation Using Adaptive Donkey and Smuggler Optimization Algorithm 认领 引用 被引量：1
13
作者 P.Prabu K.Venkatachalam +3 位作者 Ala Saleh Alluhaidan Radwa Marzouk Myriam Hadjouni Sahar A.El_Rahman 《Computers, Materials & Continua》 SCIE EI 2022年第4期1133-1152,共20页
COVID’19 has caused the entire universe to be in existential healthcrisis by spreading globally in the year 2020. The lungs infection is detected inComputed Tomography (CT) images which provide the best way to increa... COVID’19 has caused the entire universe to be in existential healthcrisis by spreading globally in the year 2020. The lungs infection is detected inComputed Tomography (CT) images which provide the best way to increasethe existing healthcare schemes in preventing the deadly virus. Nevertheless,separating the infected areas in CT images faces various issues such as lowintensity difference among normal and infectious tissue and high changes inthe characteristics of the infection. To resolve these issues, a new inf-Net (LungInfection Segmentation Deep Network) is designed for detecting the affectedareas from the CT images automatically. For the worst segmentation results,the Edge-Attention Representation (EAR) is optimized using AdaptiveDonkey and Smuggler Optimization (ADSO). The edges which are identifiedby the ADSO approach is utilized for calculating dissimilarities. An IFCM(Intuitionistic Fuzzy C-Means) clustering approach is applied for computingthe similarity of the EA component among the generated edge maps andGround-Truth (GT) edge maps. Also, a Semi-Supervised Segmentation(SSS) structure is designed using the Randomly Selected Propagation (RP)technique and Inf-Net, which needs only less number of images and unlabelleddata. Semi-Supervised Multi-Class Segmentation (SSMCS) is designed usinga Bi-LSTM (Bi-Directional Long-Short-Term-memory), acquires all theadvantages of the disease segmentation done using Semi Inf-Net and enhancesthe execution of multi-class disease labelling. The newly designed SSMCSapproach is compared with existing U-Net++, MCS, and Semi-Inf-Net.factors such as MAE (Mean Absolute Error), Structure measure, Specificity(Spec), Dice Similarity coefficient, Sensitivity (Sen), and Enhance-AlignmentMeasure are considered for evaluation purpose. 展开更多
关键词 Adaptive donkey and snuggler optimization.bi-directional long short term memory coronavirus disease 2019 randomly selected propagation semi-supervised learning
暂未订购 下载PDF
Adaptive Multi-Objective Optimization Based on Feedback Design 认领 引用
14
作者 窦立谦 宗群 +1 位作者 吉月辉 曾凡琳 《Transactions of Tianjin University》 EI CAS 2010年第5期359-365,共7页
The problem of adaptive multi-objective optimization(AMOO) has received extensive attention due to its practical significance.An important issue in optimizing a multi-objective system is adjusting the weighting coeffi... The problem of adaptive multi-objective optimization(AMOO) has received extensive attention due to its practical significance.An important issue in optimizing a multi-objective system is adjusting the weighting coefficients of multiple objectives so as to keep track of various conditions.In this paper,a feedback structure for AMOO is designed.Moreover,the reinforcement learning combined with hidden biasing information is applied to online tuning weighting coefficients of objective functions.Finally,the prop... 展开更多
关键词 multi-objective optimization adaptive optimization reinforcement learning elevator group system
暂未订购 下载PDF
Study on Adaptive Shape Optimization 认领 引用
15
作者 邹文胜 左正兴 +1 位作者 范志刚 张儒华 《Journal of Beijing Institute of Technology》 EI CAS 1998年第2期154-160,共7页
Aim To introduce a new method of adaptive shape optimization (ASOP) based on three-dimensional structure boundary strength and optimize an engine bearing cap with the method. Methods Using the normal substance's p... Aim To introduce a new method of adaptive shape optimization (ASOP) based on three-dimensional structure boundary strength and optimize an engine bearing cap with the method. Methods Using the normal substance's property of thermal expansion and cooling shrinkage,the load which is proportional to the difference between the nodes' stress and their respective objective stress were applied to the corresponding variable nodes on the boundary.The thermal load made the nodes whose stress is greater than their objective stress expand along the boundary's normal direction and the nodes whose stress is less than objec- tive stress shrink in the opposite direction , This process would repeat until the stress on the boundary nodes was converge to the objective stress. Results The satisfied results have been obtained when optimizing an engine bearing cap.The mass of the bearing cap is reduced to 55 percent of the total. Conclusion ASOP is an efficient,practical and reliable method which is suitable for optimizing the shape of the continuous structures. 展开更多
关键词 adaptive shape optimization structure strength bearing cap
暂未订购 下载PDF
Prediction model for permeability index by integrating case-based reasoning with adaptive particle swarm optimization 认领 引用
16
作者 朱红求 《High Technology Letters》 EI CAS 2009年第3期267-271,共5页
To effectively predict the permeability index of smelting process in the imperial smelting furnace, an intelligent prediction model is proposed. It integrates the case-based reasoning （CBR） with adaptive par- ticle ... To effectively predict the permeability index of smelting process in the imperial smelting furnace, an intelligent prediction model is proposed. It integrates the case-based reasoning （CBR） with adaptive par- ticle swarm optimization （PSO）. The nmnber of nearest neighbors and the weighted features vector are optimized online using the adaptive PSO to improve the prediction accuracy of CBR. The adaptive inertia weight and mutation operation are used to overcome the premature convergence of the PSO. The proposed method is validated a compared with the basic weighted CBR. The results show that the proposed model has higher prediction accuracy and better performance than the basic CBR model. 展开更多
关键词 lead and zinc smelting permeability index prediction case-based reasoning （CBR） adaptive particle swarm optimization （PS0）
暂未订购 下载PDF
Stochastic Ranking Improved Teaching-Learning and Adaptive Grasshopper Optimization Algorithm-Based Clustering Scheme for Augmenting Network Lifetime in WSNs 认领 引用 被引量：2
17
作者 N Tamilarasan SB Lenin +1 位作者 P Mukunthan NC Sendhilkumar 《China Communications》 SCIE CSCD 2024年第9期159-178,共20页
In Wireless Sensor Networks(WSNs),Clustering process is widely utilized for increasing the lifespan with sustained energy stability during data transmission.Several clustering protocols were devised for extending netw... In Wireless Sensor Networks(WSNs),Clustering process is widely utilized for increasing the lifespan with sustained energy stability during data transmission.Several clustering protocols were devised for extending network lifetime,but most of them failed in handling the problem of fixed clustering,static rounds,and inadequate Cluster Head(CH)selection criteria which consumes more energy.In this paper,Stochastic Ranking Improved Teaching-Learning and Adaptive Grasshopper Optimization Algorithm(SRITL-AGOA)-based Clustering Scheme for energy stabilization and extending network lifespan.This SRITL-AGOA selected CH depending on the weightage of factors such as node mobility degree,neighbour's density distance to sink,single-hop or multihop communication and Residual Energy(RE)that directly influences the energy consumption of sensor nodes.In specific,Grasshopper Optimization Algorithm(GOA)is improved through tangent-based nonlinear strategy for enhancing the ability of global optimization.On the other hand,stochastic ranking and violation constraint handling strategies are embedded into Teaching-Learning-based Optimization Algorithm(TLOA)for improving its exploitation tendencies.Then,SR and VCH improved TLOA is embedded into the exploitation phase of AGOA for selecting better CH by maintaining better balance amid exploration and exploitation.Simulation results confirmed that the proposed SRITL-AGOA improved throughput by 21.86%,network stability by 18.94%,load balancing by 16.14%with minimized energy depletion by19.21%,compared to the competitive CH selection approaches. 展开更多
关键词 Adaptive Grasshopper Optimization Algorithm(AGOA) Cluster Head(CH) network lifetime Teaching-Learning-based Optimization Algorithm(TLOA) Wireless Sensor Networks(WSNs)
暂未订购 下载PDF
Adaptive backtracking search optimization algorithm with pattern search for numerical optimization 认领 引用 被引量：8
18
作者 Shu Wang Xinyu Da +1 位作者 Mudong Li Tong Han 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2016年第2期395-406,共12页
The backtracking search optimization algorithm（BSA） is one of the most recently proposed population-based evolutionary algorithms for global optimization. Due to its memory ability and simple structure, BSA has powe... The backtracking search optimization algorithm（BSA） is one of the most recently proposed population-based evolutionary algorithms for global optimization. Due to its memory ability and simple structure, BSA has powerful capability to find global optimal solutions. However, the algorithm is still insufficient in balancing the exploration and the exploitation. Therefore, an improved adaptive backtracking search optimization algorithm combined with modified Hooke-Jeeves pattern search is proposed for numerical global optimization. It has two main parts： the BSA is used for the exploration phase and the modified pattern search method completes the exploitation phase. In particular, a simple but effective strategy of adapting one of BSA＇s important control parameters is introduced. The proposed algorithm is compared with standard BSA, three state-of-the-art evolutionary algorithms and three superior algorithms in IEEE Congress on Evolutionary Computation 2014（IEEE CEC2014） over six widely-used benchmarks and 22 real-parameter single objective numerical optimization benchmarks in IEEE CEC2014. The results of experiment and statistical analysis demonstrate the effectiveness and efficiency of the proposed algorithm. 展开更多
关键词 evolutionary algorithm backtracking search optimization algorithm（BSA） Hooke-Jeeves pattern search parameter adaption numerical optimization
暂未订购 下载PDF
Hybrid Optimization Algorithm for Resource Allocation in LTE-Based D2D Communication 认领 引用 被引量：1
19
作者 Amel Austine RSuji Pramila 《Computer Systems Science & Engineering》 SCIE EI 2023年第8期2263-2276,共14页
In a cellular network,direct Device-to-Device(D2D)communication enhances Quality of Service(QoS)in terms of coverage,throughput and amount of power consumed.Since theD2D pairs involve cellular resources for communicat... In a cellular network,direct Device-to-Device(D2D)communication enhances Quality of Service(QoS)in terms of coverage,throughput and amount of power consumed.Since theD2D pairs involve cellular resources for communication,the chances of interference are high.D2D communications demand minimum interference along with maximum throughput and sum rate which can be achieved by employing optimal resources and efficient power allocation procedures.In this research,a hybrid optimization model called Genetic Algorithm-Adaptive Bat Optimization(GA-ABO)algorithm is proposed for efficient resource allocation in a cellular network with D2D communication.Simulation analysis demonstrates that the proposed model involves reduced interference with maximum sum rate and throughput.The performance of the proposed model is compared with the existing Ant Colony Optimization-based resource exchange and GAME(ACO-GAME)theory models,Trader-assistedResource EXchange mechanism-RadioAccess Network(TREX-RAN)and De-centralized Radio Access Network(TREXDRAN),and greedy CYcle-Complete preferences(CYC)models.The proposed model offers a maximum sum rate of 83 kB/s,which is much better than the existing techniques. 展开更多
关键词 Device-to-Device communication Genetic Algorithm(GA) Adaptive Bat Optimization(ABO)algorithm resource exchange
暂未订购 下载PDF
Quantum particle swarm optimization for micro-grid system with consideration of consumer satisfaction and benefit of generation side 认领 引用 被引量：1
20
作者 LU Xiaojuan CAO Kai GAO Yunbo 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2021年第1期83-92,共10页
Considering comprehensive benefit of micro-grid system and consumers,we establish a mathematical model with the goal of the maximum consumer satisfaction and the maximum benefit of power generation side in the view of... Considering comprehensive benefit of micro-grid system and consumers,we establish a mathematical model with the goal of the maximum consumer satisfaction and the maximum benefit of power generation side in the view of energy management.An improved multi-objective local mutation adaptive quantum particle swarm optimization(MO-LM-AQPSO)algorithm is adopted to obtain the Pareto frontier of consumer satisfaction and the benefit of power generation side.The optimal solution of the non-dominant solution is selected with introducing the power shortage and power loss to maximize the benefit of power generation side,and its reasonableness is verified by numerical simulation.Then,translational load and time-of-use electricity price incentive mechanism are considered and reasonable peak-valley price ratio is adopted to guide users to actively participate in demand response.The simulation results show that the reasonable incentive mechanism increases the benefit of power generation side and improves the consumer satisfaction.Also the mechanism maximizes the utilization of renewable energy and effectively reduces the operation cost of the battery. 展开更多
关键词 micro-grid system consumer satisfaction benefit of power generation side time-of-use electricity price multi-objective local mutation adaptive quantum particle swarm optimization(MO-LM-AQPSO)
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 … 4 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈