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Double deep network-based traffic signal optimization method for isolated intersections 认领 引用
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作者 Rongjian Dai Yanzhen Li 《Digital Transportation and Safety》 2026年第1期42-52,共11页
This study addresses the limitations of existing reinforcement learning(RL)-based traffic signal control methods,which typically optimize either the signal phase sequence or phase duration independently.We propose a n... This study addresses the limitations of existing reinforcement learning(RL)-based traffic signal control methods,which typically optimize either the signal phase sequence or phase duration independently.We propose a novel joint optimization framework based on the Double Deep Q-Network(DDQN)that simultaneously determines both the phase sequence and phase duration.To ensure stability,the base phase duration is determined using the classical Webster method.Furthermore,a hybrid state representation is developed by integrating both microscopic and macroscopic traffic features,such as queue length and vehicle delay.A Squeeze-and-Excitation(SE)attention mechanism is introduced to guide the agent's attention toward critical traffic attributes.Simulation experiments conducted on the SUMO platform demonstrate that the proposed method significantly reduces average queue length and vehicle travel time when compared to traditional fixed-time and vehicle-actuated control strategies,particularly under medium to high traffic demand.The results validate the effectiveness,robustness,and practical applicability of the method for intelligent signal control in complex urban intersections. 展开更多
关键词 Traffic signal control Reinforcement learning Double Deep Q-Network Phase sequence Phase duration
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Resource Allocation in V2X Networks:A Double Deep Q-Network Approach with Graph Neural Networks 认领 引用
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作者 Zhengda Huan Jian Sun +3 位作者 Zeyu Chen Ziyi Zhang Xiao Sun Zenghui Xiao 《Computers, Materials & Continua》 SCIE EI 2025年第9期5427-5443,共17页
With the advancement of Vehicle-to-Everything(V2X)technology,efficient resource allocation in dynamic vehicular networks has become a critical challenge for achieving optimal performance.Existing methods suffer from h... With the advancement of Vehicle-to-Everything(V2X)technology,efficient resource allocation in dynamic vehicular networks has become a critical challenge for achieving optimal performance.Existing methods suffer from high computational complexity and decision latency under high-density traffic and heterogeneous network conditions.To address these challenges,this study presents an innovative framework that combines Graph Neural Networks(GNNs)with a Double Deep Q-Network(DDQN),utilizing dynamic graph structures and reinforcement learning.An adaptive neighbor sampling mechanism is introduced to dynamically select the most relevant neighbors based on interference levels and network topology,thereby improving decision accuracy and efficiency.Meanwhile,the framework models communication links as nodes and interference relationships as edges,effectively capturing the direct impact of interference on resource allocation while reducing computational complexity and preserving critical interaction information.Employing an aggregation mechanism based on the Graph Attention Network(GAT),it dynamically adjusts the neighbor sampling scope and performs attention-weighted aggregation based on node importance,ensuring more efficient and adaptive resource management.This design ensures reliable Vehicle-to-Vehicle(V2V)communication while maintaining high Vehicle-to-Infrastructure(V2I)throughput.The framework retains the global feature learning capabilities of GNNs and supports distributed network deployment,allowing vehicles to extract low-dimensional graph embeddings from local observations for real-time resource decisions.Experimental results demonstrate that the proposed method significantly reduces computational overhead,mitigates latency,and improves resource utilization efficiency in vehicular networks under complex traffic scenarios.This research not only provides a novel solution to resource allocation challenges in V2X networks but also advances the application of DDQN in intelligent transportation systems,offering substantial theoretical significance and practical value. 展开更多
关键词 Resource allocation V2X double deep Q-network graph neural network
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Intelligent Voltage Control Method in Active Distribution Networks Based on Averaged Weighted Double Deep Q-network Algorithm 认领 引用 被引量：2
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作者 Yangyang Wang Meiqin Mao +1 位作者 Liuchen Chang Nikos D.Hatziargyriou 《Journal of Modern Power Systems and Clean Energy》 SCIE EI CSCD 2023年第1期132-143,共12页
High penetration of distributed renewable energy sources and electric vehicles(EVs)makes future active distribution network(ADN)highly variable.These characteristics put great challenges to traditional voltage control... High penetration of distributed renewable energy sources and electric vehicles(EVs)makes future active distribution network(ADN)highly variable.These characteristics put great challenges to traditional voltage control methods.Voltage control based on the deep Q-network(DQN)algorithm offers a potential solution to this problem because it possesses humanlevel control performance.However,the traditional DQN methods may produce overestimation of action reward values,resulting in degradation of obtained solutions.In this paper,an intelligent voltage control method based on averaged weighted double deep Q-network(AWDDQN)algorithm is proposed to overcome the shortcomings of overestimation of action reward values in DQN algorithm and underestimation of action reward values in double deep Q-network(DDQN)algorithm.Using the proposed method,the voltage control objective is incorporated into the designed action reward values and normalized to form a Markov decision process(MDP)model which is solved by the AWDDQN algorithm.The designed AWDDQN-based intelligent voltage control agent is trained offline and used as online intelligent dynamic voltage regulator for the ADN.The proposed voltage control method is validated using the IEEE 33-bus and 123-bus systems containing renewable energy sources and EVs,and compared with the DQN and DDQN algorithms based methods,and traditional mixed-integer nonlinear program based methods.The simulation results show that the proposed method has better convergence and less voltage volatility than the other ones. 展开更多
关键词 Averaged weighted double deep Q-network(AWDDQN) deep Q learning active distribution network(ADN) voltage control electrical vehicle(EV)
Transformer-Aided Deep Double Dueling Spatial-Temporal Q-Network for Spatial Crowdsourcing Analysis 认领 引用
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作者 Yu Li Mingxiao Li +2 位作者 Dongyang Ou Junjie Guo Fangyuan Pan 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第4期893-909,共17页
With the rapid development ofmobile Internet,spatial crowdsourcing has becomemore andmore popular.Spatial crowdsourcing consists of many different types of applications,such as spatial crowd-sensing services.In terms ... With the rapid development ofmobile Internet,spatial crowdsourcing has becomemore andmore popular.Spatial crowdsourcing consists of many different types of applications,such as spatial crowd-sensing services.In terms of spatial crowd-sensing,it collects and analyzes traffic sensing data from clients like vehicles and traffic lights to construct intelligent traffic prediction models.Besides collecting sensing data,spatial crowdsourcing also includes spatial delivery services like DiDi and Uber.Appropriate task assignment and worker selection dominate the service quality for spatial crowdsourcing applications.Previous research conducted task assignments via traditional matching approaches or using simple network models.However,advanced mining methods are lacking to explore the relationship between workers,task publishers,and the spatio-temporal attributes in tasks.Therefore,in this paper,we propose a Deep Double Dueling Spatial-temporal Q Network(D3SQN)to adaptively learn the spatialtemporal relationship between task,task publishers,and workers in a dynamic environment to achieve optimal allocation.Specifically,D3SQNis revised through reinforcement learning by adding a spatial-temporal transformer that can estimate the expected state values and action advantages so as to improve the accuracy of task assignments.Extensive experiments are conducted over real data collected fromDiDi and ELM,and the simulation results verify the effectiveness of our proposed models. 展开更多
关键词 Historical behavior analysis spatial crowdsourcing deep double dueling Q-networks
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DDQN-Based 3D Path Planning Algorithm for UAVs in Dynamic Dense Obstacle Environments 认领 引用
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作者 Wenjie Zhang Meng Yu Yin Wang 《Journal of Beijing Institute of Technology》 EI CAS 2026年第1期84-96,共13页
Online three-dimensional(3D)path planning in dynamic environments is a fundamental problem for achieving autonomous navigation of unmanned aerial vehicles(UAVs).However,existing methods struggle to model traversable d... Online three-dimensional(3D)path planning in dynamic environments is a fundamental problem for achieving autonomous navigation of unmanned aerial vehicles(UAVs).However,existing methods struggle to model traversable dynamic gaps,resulting in conservative and suboptimal trajectories.To address these challenges,this paper proposes a hierarchical reinforcement learning(RL)framework that integrates global path guidance,local trajectory generation,predictive safety evaluation,and neural network-based decision-making.Specifically,the global planner provides long-term navigation guidance,and the local module then utilizes an improved 3D dynamic window approach(DWA)to generate dynamically feasible candidate trajectories.To enhance safety in dense dynamic scenarios,the algorithm introduces a predictive axis-aligned bounding box(AABB)strategy to model the future occupancy of obstacles,combined with convex hull verification for efficient trajectory safety assessment.Furthermore,a double deep Q-network(DDQN)is employed with structured feature encoding,enabling the neural network to reliably select the optimal trajectory from the candidate set,thereby improving robustness and generalization.Comparative experiments conducted in a high-fidelity simulation environment show that the algorithm outperforms existing algorithms,reducing the average number of collisions to 0.2 while shortening the average task completion time by approximately 15%,and achieving a success rate of 97%. 展开更多
关键词 unmanned aerial vehicle(UAV)three-dimensional(3D)path planning 3D dynamic window approach(DWA) predictive axis-aligned bounding box(AABB) double deep Q-network(DDQN) autonomous navigation
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Deep-LSDGA:以域名长度择支的双分支DGA域名检测方法 认领 引用
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作者 马安泉 张冬冬 +2 位作者 魏斯凝 李鑫 董亚鑫 《小型微型计算机系统》 CSCD 北大核心 2026年第6期1500-1508,共9页
针对现有方法在检测不同长度算法生成域名时存在较大性能差异的问题,提出一种以域名长度择支的双分支DGA域名检测方法Deep-LSDGA.数据预处理阶段,通过熵值量化字符分布特征并结合位置编码的序列表征,创新设计基于位置编码-香农熵的动态... 针对现有方法在检测不同长度算法生成域名时存在较大性能差异的问题,提出一种以域名长度择支的双分支DGA域名检测方法Deep-LSDGA.数据预处理阶段,通过熵值量化字符分布特征并结合位置编码的序列表征,创新设计基于位置编码-香农熵的动态子域选择算法,精准定位检测对象.检测模块采用以域名长度进行择支的策略:针对短域名,构建融合挤压-激励注意力机制的多尺度残差CNN(Residual-CNN-SE),强化细微特征的提取能力以提升检测精度;对于长域名,采用上下文感知的Bi-LSTM网络(Context-Bi-LSTM),序列语义关联的强化能减少模型参数量并提升处理效率.此外,引入t-分布邻域嵌入算法(t-SNE)对字符嵌入层输出的高维向量进行降维,借助散点图将域名中字符的关联关系可视化,从而确保模型正确学习域名语义并挖掘潜在关联.实验表明,Deep-LSDGA在二分类检测中各项指标均优于现有方法.家族多分类任务,准确率提升3.1%~8.2%,F1-score权重平均提升2.6%~7.2%,F1-score宏平均提升13.8%-20.4%. 展开更多
关键词 域名生成算法 网络安全 深度学习 特征提取 双分支
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Path Planning for Unmanned Surface Vehicles in Dynamic Environments Based on Artificial Potential Field and Global Guided Reinforcement Learning 认领 引用 被引量：2
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作者 Shanqiang Li Chaoxi Li 《哈尔滨工程大学学报（英文版）》 CSCD 2026年第2期575-586,共12页
For unmanned surface vehicles(USVs),how to find an effective,feasible path that substantially improves mission success rates and time efficiency in dynamic marine environments is a critical issue.To address the path p... For unmanned surface vehicles(USVs),how to find an effective,feasible path that substantially improves mission success rates and time efficiency in dynamic marine environments is a critical issue.To address the path planning problem for USVs using deep reinforcement learning(DRL)in dynamic ocean environments,an improved algorithm based on Deep Q-Networks(DQN)is proposed,which is called Fast Guided Deep Q-Network Algorithm(FG-DQN).This algorithm combines DQN with the artificial potential field(APF)method and uses the A*algorithm to initialize a guiding path in a global static environment and to provide prior knowledge for the USVs.Additionally,the configuration of the reward function using APF and the guiding path effectively reduces the frequency of random movements during the early exploration phase of the DQN algorithm,which accelerates convergence,improves the computational efficiency of path planning,and increases path safety.Finally,the performance of the presented algorithm is validated through experiments in a 2D environment.Compared with traditional reinforcement learning methods such as Q-learning and Sarsa,as well as the original DQN algorithm,FG-DQN is more effective for USV path planning. 展开更多
关键词 Deep reinforcement learning Path planning Unmanned surface vehicles Fast guided deep Q-Network algorithm
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基于强化学习自适应自耦PID的可控永磁悬浮系统控制研究 认领 引用
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作者 胡坤 孙亚伟 +2 位作者 蒋浩 陈卓 许刚 《中南大学学报（自然科学版）》 EI CSCD 北大核心 2026年第7期3043-3059,共17页
可控永磁悬浮带式输送机是一种以可控永磁悬浮托辊作为支承结构的新型磁悬浮带式输送机。针对可控永磁悬浮系统存在的非线性和易受干扰问题,提出了一种融合强化学习深度确定性策略梯度(DDPG)与自耦PID(SC-PID)的控制方法,DDPG算法作为... 可控永磁悬浮带式输送机是一种以可控永磁悬浮托辊作为支承结构的新型磁悬浮带式输送机。针对可控永磁悬浮系统存在的非线性和易受干扰问题,提出了一种融合强化学习深度确定性策略梯度(DDPG)与自耦PID(SC-PID)的控制方法,DDPG算法作为上层控制策略,通过学习可控永磁悬浮系统的非线性特性,自适应调整下层SC-PID控制的速度因子参数;对于DDPG算法训练前期探索效率低的问题,引入双经验回放池机制,提高算法前期训练效率。仿真与实验结果表明:与PID与SC-PID控制方法相比,所提出的控制方法可以根据系统跟踪误差快速调整控制参数,具有良好的跟踪性能与抗干扰性能;在外部负载冲击扰动下,系统悬浮气隙变化量为0.29 mm,较PID与SC-PID控制方法分别降低32.5%和19.4%,提升了可控永磁悬浮系统的动态性能,证明了控制器参数的智能化调整所带来的优越性。 展开更多
关键词 可控永磁悬浮系统 DDPG算法 自耦PID 双经验回放池
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“双低”扫描结合深度学习图像重建算法在肥胖患者CCTA中的可行性研究 认领 引用
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作者 张伟杰 巴竺飞 +3 位作者 张会 翟宁 邹建军 张惠英 《标记免疫分析与临床》 CAS 2026年第4期824-828,共5页
目的探讨双低扫描技术联合高权重深度学习图像重建算法(DLIR-H)在I、II级肥胖患者CCTA中的可行性。方法选取本院2024年4月至2025年3月行CCTA检查且体重指数(BMI)在28~37kg/m2之间的肥胖患者为研究对象,总共80例,随机分为两组,每组40... 目的探讨双低扫描技术联合高权重深度学习图像重建算法(DLIR-H)在I、II级肥胖患者CCTA中的可行性。方法选取本院2024年4月至2025年3月行CCTA检查且体重指数(BMI)在28~37kg/m2之间的肥胖患者为研究对象,总共80例,随机分为两组,每组40例。常规组管电压为100kVp,对比剂用量为1.0mL/kg,重建方法采用自适应性迭代重建技术(ASiR-V),权重设置为前置40%+后置60%。低剂量组管电压为80kVp,对比剂用量仍为1.0mL/kg,但是体重参考的是去脂体重(LBW),计算公式为:女性=1.07×体重-148×(体重/身高)2;男性=1.10×体重-128×(体重/身高)2,重建方法采用DLIR-H。比较两组患者辐射剂量、对比剂用量及图像质量。结果低剂量组的容积CT剂量指数(CTDIvol)、剂量长度乘积(DLP)、有效辐射剂量(ED)及对比剂用量均低于常规组,差异有统计学意义(P0.05),图像质量无差别。结论低管电压、低对比剂用量扫描联合DLIR-H在I、II级肥胖患者CCTA中可行,获得的图像质量与常规扫描相当,能够满足临床诊断要求,值得在临床推广。 展开更多
关键词 “双低”扫描 深度学习图像重建算法 肥胖患者 CCTA 辐射剂量
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基于深度强化学习决策的雷达干扰抑制方法 认领 引用
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作者 肖易寒 孟祥乾 陆钱融 《制导与引信》 2026年第1期22-31,共10页
针对目前雷达干扰抑制决策智能化程度低的问题,提出了一种基于双深度优先经验回放和可变贪婪算法改进的双重竞争深度Q网络(double dueling deep Q network,D3QN)决策的雷达干扰抑制方法。首先对雷达目标回波和干扰混合信号进行特征提取... 针对目前雷达干扰抑制决策智能化程度低的问题,提出了一种基于双深度优先经验回放和可变贪婪算法改进的双重竞争深度Q网络(double dueling deep Q network,D3QN)决策的雷达干扰抑制方法。首先对雷达目标回波和干扰混合信号进行特征提取;然后根据信号特征通过可变贪婪算法选择动作作用于干扰,并将动作前后的信号特征存储于双深度优先经验回放池后,经过学习决策出最优的干扰抑制策略;最后使用该策略抑制干扰后输出。实验结果表明,该方法有效改善了信号的脉压结果,显著提升了信号的信干噪比,相较于基于D3QN的传统干扰抑制方法,在策略准确率和收敛速度上分别提升了7.3%和8.7%。 展开更多
关键词 雷达干扰抑制 双重竞争深度Q网络 双深度优先经验回放 可变贪婪算法 脉冲压缩
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Autonomous maneuver decision-making for a UCAV in short-range aerial combat based on an MS-DDQN algorithm 认领 引用 被引量：17
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作者 Yong-feng Li Jing-ping Shi +2 位作者 Wei Jiang Wei-guo Zhang Yong-xi Lyu 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2022年第9期1697-1714,共18页
To solve the problem of realizing autonomous aerial combat decision-making for unmanned combat aerial vehicles(UCAVs) rapidly and accurately in an uncertain environment, this paper proposes a decision-making method ba... To solve the problem of realizing autonomous aerial combat decision-making for unmanned combat aerial vehicles(UCAVs) rapidly and accurately in an uncertain environment, this paper proposes a decision-making method based on an improved deep reinforcement learning(DRL) algorithm: the multistep double deep Q-network(MS-DDQN) algorithm. First, a six-degree-of-freedom UCAV model based on an aircraft control system is established on a simulation platform, and the situation assessment functions of the UCAV and its target are established by considering their angles, altitudes, environments, missile attack performances, and UCAV performance. By controlling the flight path angle, roll angle, and flight velocity, 27 common basic actions are designed. On this basis, aiming to overcome the defects of traditional DRL in terms of training speed and convergence speed, the improved MS-DDQN method is introduced to incorporate the final return value into the previous steps. Finally, the pre-training learning model is used as the starting point for the second learning model to simulate the UCAV aerial combat decision-making process based on the basic training method, which helps to shorten the training time and improve the learning efficiency. The improved DRL algorithm significantly accelerates the training speed and estimates the target value more accurately during training, and it can be applied to aerial combat decision-making. 展开更多
关键词 Unmanned combat aerial vehicle Aerial combat decision Multi-step double deep Q-network Six-degree-of-freedom Aerial combat maneuver library
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Intelligent Operation Strategies for PVT-ASHP Heating and Hot Water Systems in Industrial Parks Based on Reinforcement Learning 认领 引用
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作者 Yingjie Su Yubin Qiu +2 位作者 Zhuojun Dong Jiying Liu Bo Gao 《Energy Engineering》 EI 2026年第7期40-74,共35页
In response to the high energy consumption,large load fluctuations,and insufficient adaptability associated with conventional control strategies in industrial park heating and hot water systems,this paper studies a 15... In response to the high energy consumption,large load fluctuations,and insufficient adaptability associated with conventional control strategies in industrial park heating and hot water systems,this paper studies a 15,000 m2 factory office building in Jinan as its object of study.A photovoltaic-thermal integrated air-source heat pump system(PVT-ASHP)is developed.This system leverages its hardware parameter co-optimization and intelligent operational strategy control to perform cost reduction and efficiency increase,while focusing on the novel innovative high effectiveness of its operational strategies.The study first employs the Hooke-Jeeves algorithm to optimize key hardware parameters so as to minimize the annual cost,perform many adjustments,including the reduction of the PVT collector area from 931 to 799 m2,regulate the PVT tilt angle from 36°to 43°,and modify the storage tank volume.This allows for the establishment of a low-energy baseline,reducing the initial PVT equipment investment by approximately 14.2%.In addition,the PVT photovoltaic efficiency is stabilized at 14%,while the solar thermal efficiency fluctuates around 33%.The core operational strategy uses a reinforcement learning algorithm based on Deep Q-Network(DQN).Its design incorporates dual variables PVT electricity generation(PVTd)and PVT heat supply(PVTh)into the state space,which overcomes the limitations of conventional control relying solely on load and outdoor temperature to perform dynamic matching between energy production and load demand.The reward function comprises dynamic weighting for energy consumption and comfort,where the energy consumption weight and comfort weight are set to 0.9 and O.l,respectively.Based on the office hours of the factory(8:0o-18:0o as high load,and non-office hours as low load),an hourly load input mechanism is designed to remove the control deviations caused by the average load assumption.Simulations are then conducted.The obtained results demonstrate that,compared with the conventional control strategy of fixed temperature at 6OoC,the designed DQN reinforcement learning operation strategy achieves energy savings of about 2.99%.During office hours,the system maintains a stable supply water temperature of 57°C,which is consistent with comfort requirements while avoiding energy waste.After performing parameter optimization using the operational control strategy,the annual operating costs of the system decrease by 9.43%,while significantly increasing the overall energy efficiency.This paper demonstrates that the proposed DQN reinforcement learning operation strategy,tailored to the load characteristics of factory campuses,plays an important role in improving system performance.Based on the principles of dynamic perception,precise matching,and demand-driven regulation,it provides a potential reference framework for designing similar systems to ensure the efficient operation of distributed energy systems in factory campus-type buildings. 展开更多
关键词 Transient system simulation program Deep Q-Network Hooke-Jeeves algorithm generic optimization program heat supply
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Automatic depth matching method of well log based on deep reinforcement learning 认领 引用 被引量：7
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作者 XIONG Wenjun XIAO Lizhi +1 位作者 YUAN Jiangru YUE Wenzheng 《Petroleum Exploration and Development》 SCIE 2024年第3期634-646,共13页
In the traditional well log depth matching tasks,manual adjustments are required,which means significantly labor-intensive for multiple wells,leading to low work efficiency.This paper introduces a multi-agent deep rei... In the traditional well log depth matching tasks,manual adjustments are required,which means significantly labor-intensive for multiple wells,leading to low work efficiency.This paper introduces a multi-agent deep reinforcement learning(MARL)method to automate the depth matching of multi-well logs.This method defines multiple top-down dual sliding windows based on the convolutional neural network(CNN)to extract and capture similar feature sequences on well logs,and it establishes an interaction mechanism between agents and the environment to control the depth matching process.Specifically,the agent selects an action to translate or scale the feature sequence based on the double deep Q-network(DDQN).Through the feedback of the reward signal,it evaluates the effectiveness of each action,aiming to obtain the optimal strategy and improve the accuracy of the matching task.Our experiments show that MARL can automatically perform depth matches for well-logs in multiple wells,and reduce manual intervention.In the application to the oil field,a comparative analysis of dynamic time warping(DTW),deep Q-learning network(DQN),and DDQN methods revealed that the DDQN algorithm,with its dual-network evaluation mechanism,significantly improves performance by identifying and aligning more details in the well log feature sequences,thus achieving higher depth matching accuracy. 展开更多
关键词 artificial intelligence machine learning depth matching well log multi-agent deep reinforcement learning convolutional neural network double deep Q-network
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Deep reinforcement learning for UAV swarm rendezvous behavior 认领 引用 被引量：2
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作者 ZHANG Yaozhong LI Yike +1 位作者 WU Zhuoran XU Jialin 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2023年第2期360-373,共14页
The unmanned aerial vehicle(UAV)swarm technology is one of the research hotspots in recent years.With the continuous improvement of autonomous intelligence of UAV,the swarm technology of UAV will become one of the mai... The unmanned aerial vehicle(UAV)swarm technology is one of the research hotspots in recent years.With the continuous improvement of autonomous intelligence of UAV,the swarm technology of UAV will become one of the main trends of UAV development in the future.This paper studies the behavior decision-making process of UAV swarm rendezvous task based on the double deep Q network(DDQN)algorithm.We design a guided reward function to effectively solve the problem of algorithm convergence caused by the sparse return problem in deep reinforcement learning(DRL)for the long period task.We also propose the concept of temporary storage area,optimizing the memory playback unit of the traditional DDQN algorithm,improving the convergence speed of the algorithm,and speeding up the training process of the algorithm.Different from traditional task environment,this paper establishes a continuous state-space task environment model to improve the authentication process of UAV task environment.Based on the DDQN algorithm,the collaborative tasks of UAV swarm in different task scenarios are trained.The experimental results validate that the DDQN algorithm is efficient in terms of training UAV swarm to complete the given collaborative tasks while meeting the requirements of UAV swarm for centralization and autonomy,and improving the intelligence of UAV swarm collaborative task execution.The simulation results show that after training,the proposed UAV swarm can carry out the rendezvous task well,and the success rate of the mission reaches 90%. 展开更多
关键词 double deep Q network(DDQN)algorithms unmanned aerial vehicle(UAV)swarm task decision deep reinforcement learning(DRL) sparse returns
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深度挖掘下能源行业物流总成本控制仿真 认领 引用
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作者 刘建爽 郜珩 +1 位作者 古恒锐 张方明 《计算机仿真》 2026年第4期91-95,共5页
能源供应链因其天然的双层结构与时空尺度的非线性耦合特性,使得决策变量呈现混沌特征,使得传统的单向静态局部优化方法难以有效协调物流网络布局与车辆调度在双重博弈空间中的协同,导致成本控制面临决策割裂、整体优化困难。为此,提出... 能源供应链因其天然的双层结构与时空尺度的非线性耦合特性,使得决策变量呈现混沌特征,使得传统的单向静态局部优化方法难以有效协调物流网络布局与车辆调度在双重博弈空间中的协同,导致成本控制面临决策割裂、整体优化困难。为此,提出基于深度挖掘的能源行业物流成本控制方法。运用改进的k-means聚类分析方法挖掘成本数据,通过建立欧几里得距离属性差异度和属性相似度指标,精准识别采购、运输、仓储等成本要素间的动态耦合关系,明确关联并划分成本;设定能源运输成本、能源损耗等参数变量,动态模拟物流运行状态,构建以能源物流成本最小化为目标的控制模型。针对能源供应链的双层结构特性,引入源于蝙蝠回声定位机制的双层蝙蝠算法,设计双层蝙蝠位置编码、构建适应度函数,通过双层速度更新和脉冲频率调整机制迭代优化,避免单层优化导致的决策割裂,实现双重博弈空间协同优化以降低成本。实验结果表明,上述方法能显著优化运输路径布局,有效减少绕行距离;在运输成本控制方面表现出更好的稳定性和更优的经济性;同时具有更快的收敛速度和更低的最终损耗值,验证了其在降低能源物流总成本方面的优越性能。 展开更多
关键词 深度挖掘 能源行业 物流成本控制 双层蝙蝠算法 车辆调度
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基于深度学习与遗传算法的铝罐二重卷封工艺参数多目标优化 认领 引用
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作者 张子璇 杨建余 +1 位作者 于培师 赵军华 《轻工机械》 CAS 2026年第2期1-10,共10页
铝罐二重卷封工艺是决定饮料包装密封可靠性与使用安全性的核心制造工序,其成形质量直接关系到产品的抗泄漏性能与设备服役寿命,为实现叠接率与紧密度的协同提升和辊轮接触应力的有效控制,课题组提出了一种融合深度前馈神经网络(Depth F... 铝罐二重卷封工艺是决定饮料包装密封可靠性与使用安全性的核心制造工序,其成形质量直接关系到产品的抗泄漏性能与设备服役寿命,为实现叠接率与紧密度的协同提升和辊轮接触应力的有效控制,课题组提出了一种融合深度前馈神经网络(Depth Feedforward Neural Network,DFNN)与非支配排序遗传算法Ⅱ(Non-dominated Sorting Genetic AlgorithmⅡ,NSGA-Ⅱ)的多目标优化方法。首先,建立高精度有限元模型,解析关键工艺参数对密封性能与力学响应的影响机制;然后,构建DFNN代理模型,实现对叠接率、波峰数及最大接触应力的精准映射;最后,采用NSGA-Ⅱ算法进行多目标优化求解,获取帕累托最优工艺参数解集。结果表明:优化方案在叠接率、紧密度与最大接触应力方面均显著优于初始设计,实验验证结果与仿真预测结果的误差小于7%。课题组所提方法在提升密封性能与延长设备寿命方面具有有效性与工程适用性。 展开更多
关键词 饮料包装 铝罐 二重卷封 多目标优化 深度学习 遗传算法
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基于DDQN算法的多无人机协同覆盖路径规划 认领 引用
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作者 李彩玲 《测绘通报》 CSCD 北大核心 2026年第5期110-116,共7页
[目的]为优化多无人机协同作业的任务完成时间并提升路径规划效率,本文提出了一种基于双深度Q网络(DDQN)算法的多无人机协同路径规划方法。[方法]首先,设计一种高效的环境信息地图融合技术,可快速、高效地标记每架无人机的覆盖记录并检... [目的]为优化多无人机协同作业的任务完成时间并提升路径规划效率,本文提出了一种基于双深度Q网络(DDQN)算法的多无人机协同路径规划方法。[方法]首先,设计一种高效的环境信息地图融合技术,可快速、高效地标记每架无人机的覆盖记录并检测障碍物位置;其次,引入双深度Q网络算法以最小化任务时间,避免路径重叠、区域遗漏及潜在碰撞;然后,构建一种新的协同学习机制,高效规划全局最优路径;最后,搭建仿真平台对所提方法进行仿真,并选取两种典型方法进行对比分析。[结果]试验结果表明,相较于其他两种对比方法,本文方法在任务完成时间和覆盖效率方面均展现出更优性能。[结论]该方法能够快速适应未知障碍物与复杂环境,在多种任务场景下实现目标区域的高效全面覆盖,具有良好的响应速度和覆盖率。 展开更多
关键词 路径规划 多无人机 双深度Q网络算法 全局搜索
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Associative Tasks Computing Offloading Scheme in Internet of Medical Things with Deep Reinforcement Learning 认领 引用 被引量：3
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作者 Jiang Fan Qin Junwei +1 位作者 Liu Lei Tian Hui 《China Communications》 SCIE CSCD 2024年第4期38-52,共15页
The Internet of Medical Things(Io MT) is regarded as a critical technology for intelligent healthcare in the foreseeable 6G era. Nevertheless, due to the limited computing power capability of edge devices and task-rel... The Internet of Medical Things(Io MT) is regarded as a critical technology for intelligent healthcare in the foreseeable 6G era. Nevertheless, due to the limited computing power capability of edge devices and task-related coupling relationships, Io MT faces unprecedented challenges. Considering the associative connections among tasks, this paper proposes a computing offloading policy for multiple-user devices(UDs) considering device-to-device(D2D) communication and a multi-access edge computing(MEC)technique under the scenario of Io MT. Specifically,to minimize the total delay and energy consumption concerning the requirement of Io MT, we first analyze and model the detailed local execution, MEC execution, D2D execution, and associated tasks offloading exchange model. Consequently, the associated tasks’ offloading scheme of multi-UDs is formulated as a mixed-integer nonconvex optimization problem. Considering the advantages of deep reinforcement learning(DRL) in processing tasks related to coupling relationships, a Double DQN based associative tasks computing offloading(DDATO) algorithm is then proposed to obtain the optimal solution, which can make the best offloading decision under the condition that tasks of UDs are associative. Furthermore, to reduce the complexity of the DDATO algorithm, the cacheaided procedure is intentionally introduced before the data training process. This avoids redundant offloading and computing procedures concerning tasks that previously have already been cached by other UDs. In addition, we use a dynamic ε-greedy strategy in the action selection section of the algorithm, thus preventing the algorithm from falling into a locally optimal solution. Simulation results demonstrate that compared with other existing methods for associative task models concerning different structures in the Io MT network, the proposed algorithm can lower the total cost more effectively and efficiently while also providing a tradeoff between delay and energy consumption tolerance. 展开更多
关键词 associative tasks cache-aided procedure double deep Q-network Internet of Medical Things(IoMT) multi-access edge computing(MEC)
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双深度多层穿梭车仓储系统倒货策略与作业调度方法 认领 引用
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作者 周丰旭 刘飞 范国良 《机电工程》 CAS 北大核心 2026年第2期370-381,共12页
双深度多层穿梭车仓储系统普遍存在倒货作业,导致出入库作业时间增加,系统作业效率降低。随着货位占用率的上升,倒货作业调度的难度和复杂度持续增加。针对这一问题,提出了一种双深度多层穿梭车仓储系统倒货策略与作业调度方法。首先,... 双深度多层穿梭车仓储系统普遍存在倒货作业,导致出入库作业时间增加,系统作业效率降低。随着货位占用率的上升,倒货作业调度的难度和复杂度持续增加。针对这一问题,提出了一种双深度多层穿梭车仓储系统倒货策略与作业调度方法。首先,分析了倒货作业过程,提出了随机点倒货策略、最近点倒货策略和固定点倒货策略三种倒货作业策略,建立了倒货作业时间模型和任务调度出库作业时间模型;然后,以出库作业时间最小为目标,建立了出库作业调度优化模型;接着,设计了双种群遗传算法对模型进行了求解,引入了变邻域搜索及双种群重组和协作优化策略,增加了算法寻优能力,提升了算法搜索性能;最后,采用案例分析了倒货策略和作业调度方法的有效性,开展了算法对比分析以验证算法的优越性。研究结果表明:调度任务规模从35提高到100时,算法优化效率从13.28%提升到24.26%,双种群遗传算法的优化效率更高,能够有效缩短出库作业时间。集成倒货策略的调度优化方法能够准确评估倒货作业时间,进而提升双深度多层穿梭车仓储系统作业效率。 展开更多
关键词 双深度多层穿梭车仓储系统 倒货作业 变邻域搜索 遗传算法 随机点倒货策略 最近点倒货策略 固定点倒货策略
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基于改进近端策略优化算法的柔性作业车间调度 认领 引用 被引量：9
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作者 王艳红 付威通 +2 位作者 张俊 谭园园 田中大 《控制与决策》 EI CSCD 北大核心 2025年第6期1883-1891,共9页
柔性作业车间调度是经典且复杂的组合优化问题,对于离散制造系统的生产优化具有重要的理论和实际意义.基于多指针图网络框架和近端策略优化算法设计一种求解柔性作业车间调度问题的深度强化学习算法.首先,将“工序-机器”分配调度过程... 柔性作业车间调度是经典且复杂的组合优化问题,对于离散制造系统的生产优化具有重要的理论和实际意义.基于多指针图网络框架和近端策略优化算法设计一种求解柔性作业车间调度问题的深度强化学习算法.首先,将“工序-机器”分配调度过程表征成由选择工序和分配机器两类动作构成的马尔可夫决策过程;其次,通过解耦策略解除动作之间的耦合关系,并设计新的损失函数和贪婪采样策略以提高算法的验证推理能力;在此基础上扩充状态空间,使评估网络能够更全面地感知与评估,从而进一步提升算法的学习和决策能力.在随机生成算例及基准算例上进行仿真和对比分析,验证算法的良好性能及泛化能力. 展开更多
关键词 柔性作业车间调度 近端策略优化算法 双动作耦合网络 损失函数优化 贪婪采样 深度强化学习
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