This paper investigates a distributed heterogeneous hybrid blocking flow-shop scheduling problem(DHHBFSP)designed to minimize the total tardiness and total energy consumption simultaneously,and proposes an improved pr...This paper investigates a distributed heterogeneous hybrid blocking flow-shop scheduling problem(DHHBFSP)designed to minimize the total tardiness and total energy consumption simultaneously,and proposes an improved proximal policy optimization(IPPO)method to make real-time decisions for the DHHBFSP.A multi-objective Markov decision process is modeled for the DHHBFSP,where the reward function is represented by a vector with dynamic weights instead of the common objectiverelated scalar value.A factory agent(FA)is formulated for each factory to select unscheduled jobs and is trained by the proposed IPPO to improve the decision quality.Multiple FAs work asynchronously to allocate jobs that arrive randomly at the shop.A two-stage training strategy is introduced in the IPPO,which learns from both single-and dual-policy data for better data utilization.The proposed IPPO is tested on randomly generated instances and compared with variants of the basic proximal policy optimization(PPO),dispatch rules,multi-objective metaheuristics,and multi-agent reinforcement learning methods.Extensive experimental results suggest that the proposed strategies offer significant improvements to the basic PPO,and the proposed IPPO outperforms the state-of-the-art scheduling methods in both convergence and solution quality.展开更多
The overall performance of multi-robot collaborative systems is significantly affected by the multi-robot task allocation.To improve the effectiveness,robustness,and safety of multi-robot collaborative systems,a multi...The overall performance of multi-robot collaborative systems is significantly affected by the multi-robot task allocation.To improve the effectiveness,robustness,and safety of multi-robot collaborative systems,a multimodal multi-objective evolutionary algorithm based on deep reinforcement learning is proposed in this paper.The improved multimodal multi-objective evolutionary algorithm is used to solve multi-robot task allo-cation problems.Moreover,a deep reinforcement learning strategy is used in the last generation to provide a high-quality path for each assigned robot via an end-to-end manner.Comparisons with three popular multimodal multi-objective evolutionary algorithms on three different scenarios of multi-robot task allocation problems are carried out to verify the performance of the proposed algorithm.The experimental test results show that the proposed algorithm can generate sufficient equivalent schemes to improve the availability and robustness of multi-robot collaborative systems in uncertain environments,and also produce the best scheme to improve the overall task execution efficiency of multi-robot collaborative systems.展开更多
Solving constrained multi-objective optimization problems with evolutionary algorithms has attracted considerable attention.Various constrained multi-objective optimization evolutionary algorithms(CMOEAs)have been dev...Solving constrained multi-objective optimization problems with evolutionary algorithms has attracted considerable attention.Various constrained multi-objective optimization evolutionary algorithms(CMOEAs)have been developed with the use of different algorithmic strategies,evolutionary operators,and constraint-handling techniques.The performance of CMOEAs may be heavily dependent on the operators used,however,it is usually difficult to select suitable operators for the problem at hand.Hence,improving operator selection is promising and necessary for CMOEAs.This work proposes an online operator selection framework assisted by Deep Reinforcement Learning.The dynamics of the population,including convergence,diversity,and feasibility,are regarded as the state;the candidate operators are considered as actions;and the improvement of the population state is treated as the reward.By using a Q-network to learn a policy to estimate the Q-values of all actions,the proposed approach can adaptively select an operator that maximizes the improvement of the population according to the current state and thereby improve the algorithmic performance.The framework is embedded into four popular CMOEAs and assessed on 42 benchmark problems.The experimental results reveal that the proposed Deep Reinforcement Learning-assisted operator selection significantly improves the performance of these CMOEAs and the resulting algorithm obtains better versatility compared to nine state-of-the-art CMOEAs.展开更多
Resource allocation is an important problem influencing the service quality of multi-beam satellite communications.In multi-beam satellite communications, the available frequency bandwidth is limited, users requiremen...Resource allocation is an important problem influencing the service quality of multi-beam satellite communications.In multi-beam satellite communications, the available frequency bandwidth is limited, users requirements vary rapidly, high service quality and joint allocation of multi-dimensional resources such as time and frequency are required. It is a difficult problem needs to be researched urgently for multi-beam satellite communications, how to obtain a higher comprehensive utilization rate of multidimensional resources, maximize the number of users and system throughput, and meet the demand of rapid allocation adapting dynamic changed the number of users under the condition of limited resources, with using an efficient and fast resource allocation algorithm.In order to solve the multi-dimensional resource allocation problem of multi-beam satellite communications, this paper establishes a multi-objective optimization model based on the maximum the number of users and system throughput joint optimization goal, and proposes a multi-objective deep reinforcement learning based time-frequency two-dimensional resource allocation(MODRL-TF) algorithm to adapt dynamic changed the number of users and the timeliness requirements. Simulation results show that the proposed algorithm could provide higher comprehensive utilization rate of multi-dimensional resources,and could achieve multi-objective joint optimization,and could obtain better timeliness than traditional heuristic algorithms, such as genetic algorithm(GA)and ant colony optimization algorithm(ACO).展开更多
The overall healthcare system has been prioritized within development top lists worldwide.Since many national populations are aging,combined with the availability of sophisticated medical treatments,healthcare expendi...The overall healthcare system has been prioritized within development top lists worldwide.Since many national populations are aging,combined with the availability of sophisticated medical treatments,healthcare expenditures are rapidly growing.Blood banks are a major component of any healthcare system,which store and provide the blood products needed for organ transplants,emergency medical treatments,and routine surgeries.Timely delivery of blood products is vital,especially in emergency settings.Hence,blood delivery process parameters such as safety and speed have received attention in the literature,as well as other parameters such as delivery cost.In this paper,delivery time and cost are modeled mathematically and marked as objective functions requiring simultaneous optimization.A solution is proposed based on Deep Reinforcement Learning(DRL)to address the formulated delivery functions as Multi-objective Optimization Problems(MOPs).The basic concept of the solution is to decompose the MOP into a scalar optimization sub-problems set,where each one of these sub-problems is modeled as a separate Neural Network(NN).The overall model parameters for each sub-problem are optimized based on a neighborhood parameter transfer and DRL training algorithm.The optimization step for the subproblems is undertaken collaboratively to optimize the overall model.Paretooptimal solutions can be directly obtained using the trained NN.Specifically,the multi-objective blood bank delivery problem is addressed in this research.Onemajor technical advantage of this approach is that once the trainedmodel is available,it can be scaled without the need formodel retraining.The scoring can be obtained directly using a straightforward computation of the NN layers in a limited time.The proposed technique provides a set of technical strength points such as the ability to generalize and solve rapidly compared to othermulti-objective optimizationmethods.The model was trained and tested on 5 major hospitals in Saudi Arabia’s Riyadh region,and the simulation results indicated that time and cost decreased by 35%and 30%,respectively.In particular,the proposed model outperformed other state-of-the-art MOP solutions such as Genetic Algorithms and Simulated Annealing.展开更多
This paper presents an improved virtual coupling train set(VCTS)operation control framework to deal with the lack of opti-mization of speed curves in the traditional techniques.The framework takes into account the tem...This paper presents an improved virtual coupling train set(VCTS)operation control framework to deal with the lack of opti-mization of speed curves in the traditional techniques.The framework takes into account the temporary speed limit on the railway line and the communication delay between trains,and it uses a VCTS consisting of three trains as an experimental object.It creates the virtual coupling train tracking and control process by improving the driving strategy of the leader train and using the leader-follower model.The follower train uses the improved speed curve of the leader train as its speed refer-ence curve through knowledge migration,and this completes the multi-objective optimization of the driving strategy for the VCTS.The experimental results confirm that the deep reinforcement learning algorithm effectively achieves the optimization goal of the train driving strategy.They also reveal that the intrinsic curiosity module prioritized experience replay dueling double deep Q-network(ICM-PER-D3QN)algorithm outperforms the deep Q-network(DQN)algorithm in optimizing the driving strategy of the leader train.The ICM-PER-D3QN algorithm enhances the leader train driving strategy by an average of 57%when compared to the DQN algorithm.Furthermore,the particle swarm optimization(PSO)-based model predictive control(MPC)algorithm has also demonstrated tracking accuracy and further improved safety during VCTS operation,with an average increase of 37.7%in tracking accuracy compared to the traditional MPC algorithm.展开更多
This research paper presents a comprehensive investigation into the effectiveness of the DeepSurNet-NSGA II(Deep Surrogate Model-Assisted Non-dominated Sorting Genetic Algorithm II)for solving complex multiobjective o...This research paper presents a comprehensive investigation into the effectiveness of the DeepSurNet-NSGA II(Deep Surrogate Model-Assisted Non-dominated Sorting Genetic Algorithm II)for solving complex multiobjective optimization problems,with a particular focus on robotic leg-linkage design.The study introduces an innovative approach that integrates deep learning-based surrogate models with the robust Non-dominated Sorting Genetic Algorithm II,aiming to enhance the efficiency and precision of the optimization process.Through a series of empirical experiments and algorithmic analyses,the paper demonstrates a high degree of correlation between solutions generated by the DeepSurNet-NSGA II and those obtained from direct experimental methods,underscoring the algorithm’s capability to accurately approximate the Pareto-optimal frontier while significantly reducing computational demands.The methodology encompasses a detailed exploration of the algorithm’s configuration,the experimental setup,and the criteria for performance evaluation,ensuring the reproducibility of results and facilitating future advancements in the field.The findings of this study not only confirm the practical applicability and theoretical soundness of the DeepSurNet-NSGA II in navigating the intricacies of multi-objective optimization but also highlight its potential as a transformative tool in engineering and design optimization.By bridging the gap between complex optimization challenges and achievable solutions,this research contributes valuable insights into the optimization domain,offering a promising direction for future inquiries and technological innovations.展开更多
With the remarkable advancements in machine vision research and its ever-expanding applications,scholars have increasingly focused on harnessing various vision methodologies within the industrial realm.Specifically,de...With the remarkable advancements in machine vision research and its ever-expanding applications,scholars have increasingly focused on harnessing various vision methodologies within the industrial realm.Specifically,detecting vehicle floor welding points poses unique challenges,including high operational costs and limited portability in practical settings.To address these challenges,this paper innovatively integrates template matching and the Faster RCNN algorithm,presenting an industrial fusion cascaded solder joint detection algorithm that seamlessly blends template matching with deep learning techniques.This algorithm meticulously weights and fuses the optimized features of both methodologies,enhancing the overall detection capabilities.Furthermore,it introduces an optimized multi-scale and multi-template matching approach,leveraging a diverse array of templates and image pyramid algorithms to bolster the accuracy and resilience of object detection.By integrating deep learning algorithms with this multi-scale and multi-template matching strategy,the cascaded target matching algorithm effectively accurately identifies solder joint types and positions.A comprehensive welding point dataset,labeled by experts specifically for vehicle detection,was constructed based on images from authentic industrial environments to validate the algorithm’s performance.Experiments demonstrate the algorithm’s compelling performance in industrial scenarios,outperforming the single-template matching algorithm by 21.3%,the multi-scale and multitemplate matching algorithm by 3.4%,the Faster RCNN algorithm by 19.7%,and the YOLOv9 algorithm by 17.3%in terms of solder joint detection accuracy.This optimized algorithm exhibits remarkable robustness and portability,ideally suited for detecting solder joints across diverse vehicle workpieces.Notably,this study’s dataset and feature fusion approach can be a valuable resource for other algorithms seeking to enhance their solder joint detection capabilities.This work thus not only presents a novel and effective solution for industrial solder joint detection but lays the groundwork for future advancements in this critical area.展开更多
Higher requirements for the accuracy of relevant models are put throughout the transformation and upgrade of the iron and steel sector to intelligent production.It has been difficult to meet the needs of the field wit...Higher requirements for the accuracy of relevant models are put throughout the transformation and upgrade of the iron and steel sector to intelligent production.It has been difficult to meet the needs of the field with the usual prediction model of mechanical properties of hotrolled strip.Insufficient data and difficult parameter adjustment limit deep learning models based on multi-layer networks in practical applications;besides,the limited discrete process parameters used make it impossible to effectively depict the actual strip processing process.In order to solve these problems,this research proposed a new sampling approach for mechanical characteristics input data of hot-rolled strip based on the multi-grained cascade forest(gcForest)framework.According to the characteristics of complex process flow and abnormal sensitivity of process path and parameters to product quality in the hot-rolled strip production,a three-dimensional continuous time series process data sampling method based on time-temperature-deformation was designed.The basic information of strip steel(chemical composition and typical process parameters)is fused with the local process information collected by multi-grained scanning,so that the next link’s input has both local and global features.Furthermore,in the multi-grained scanning structure,a sub sampling scheme with a variable window was designed,so that input data with different dimensions can get output characteristics of the same dimension after passing through the multi-grained scanning structure,allowing the cascade forest structure to be trained normally.Finally,actual production data of three steel grades was used to conduct the experimental evaluation.The results revealed that the gcForest-based mechanical property prediction model outperforms the competition in terms of comprehensive performance,ease of parameter adjustment,and ability to sustain high prediction accuracy with fewer samples.展开更多
Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrain...Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit(RSU),thereby achieving lower delay and energy consumption.However,due to the limited storage capacity and energy budget of RSUs,it is challenging to meet the demands of the highly dynamic Internet of Vehicles(IoV)environment.Therefore,determining reasonable service caching and computation offloading strategies is crucial.To address this,this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading.By modeling the dynamic optimization problem using Markov Decision Processes(MDP),the scheme jointly optimizes task delay,energy consumption,load balancing,and privacy entropy to achieve better quality of service.Additionally,a dynamic adaptive multi-objective deep reinforcement learning algorithm is proposed.Each Double Deep Q-Network(DDQN)agent obtains rewards for different objectives based on distinct reward functions and dynamically updates the objective weights by learning the value changes between objectives using Radial Basis Function Networks(RBFN),thereby efficiently approximating the Pareto-optimal decisions for multiple objectives.Extensive experiments demonstrate that the proposed algorithm can better coordinate the three-tier computing resources of cloud,edge,and vehicles.Compared to existing algorithms,the proposed method reduces task delay and energy consumption by 10.64%and 5.1%,respectively.展开更多
Several applications of machine learning and artificial intelligence,have acquired importance and come to the fore as a result of recent advances and improvements in these approaches.Autonomous cars are one such appli...Several applications of machine learning and artificial intelligence,have acquired importance and come to the fore as a result of recent advances and improvements in these approaches.Autonomous cars are one such application.This is expected to have a significant and revolutionary influence on society.Integration with smart cities,new infrastructure and urban planning with sophisticated cyber-security are some of the current ramifications of self-driving automobiles.The autonomous automobile,often known as selfdriving systems or driverless vehicles,is a vehicle that can perceive its surroundings and navigate predetermined routes without human involvement.Cars are on the verge of evolving into autonomous robots,thanks to significant breakthroughs in artificial intelligence and related technologies,and this will have a wide range of socio-economic implications.However,in order for these automobiles to become a reality,they must be endowed with the perception and cognition necessary to deal with high-pressure real-life events and make proper judgments and take appropriate action.The majority of self-driving car technologies are based on computer systems that automate vehicle control parts.From forward-collision warning and antilock brakes to lane-keeping and adaptive drive control,to fully automated driving,these technological components have a wide range of capabilities.A self-driving car combines a wide range of sensors,actuators,and cameras.Recent researches on computer vision and deep learning are used to control autonomous driving systems.For self-driving automobiles,lane-keeping is crucial.This study presents a deep learning approach to obtain the proper steering angle to maintain the robot in the lane.We propose an advanced control for a selfdriving robot by using two controllers simultaneously.Convolutional neural networks(CNNs)are employed,to predict the car’and a proportionalintegral-derivative(PID)controller is designed for speed and steering control.This study uses a Raspberry PI based camera to control the robot car.展开更多
Deep deterministic policy gradient(DDPG)has been proved to be effective in optimizing particle swarm optimization(PSO),but whether DDPG can optimize multi-objective discrete particle swarm optimization(MODPSO)remains ...Deep deterministic policy gradient(DDPG)has been proved to be effective in optimizing particle swarm optimization(PSO),but whether DDPG can optimize multi-objective discrete particle swarm optimization(MODPSO)remains to be determined.The present work aims to probe into this topic.Experiments showed that the DDPG can not only quickly improve the convergence speed of MODPSO,but also overcome the problem of local optimal solution that MODPSO may suffer.The research findings are of great significance for the theoretical research and application of MODPSO.展开更多
For complex flows in compressors containing flow separations and adverse pressure gradients,the numerical simulation results based on Reynolds-averaged Navier-Stokes(RANS)models often deviate from experimental measure...For complex flows in compressors containing flow separations and adverse pressure gradients,the numerical simulation results based on Reynolds-averaged Navier-Stokes(RANS)models often deviate from experimental measurements more or less.To improve the prediction accuracy and reduce the difference between the RANS prediction results and experimental measurements,an experimental data-driven flow field prediction method based on deep learning and l_(1)regularization is proposed and applied to a compressor cascade flow field.The inlet boundary conditions and turbulence model parameters are calibrated to obtain the high-fidelity flow fields.The Saplart-Allmaras and SST turbulence models are used independently for mutual validation.The contributions of key modified parameters are also analyzed via sensitivity analysis.The results show that the prediction error can be reduced by nearly 70%based on the proposed algorithm.The flow fields predicted by the two calibrated turbulence models are almost the same and nearly independent of the turbulence models.The corrections of the inlet boundary conditions reduce the error in the first half of the chord.The turbulence model calibrations fix the overprediction of flow separation on the suction surface near the tail edge.展开更多
混凝土内部损伤破坏形态具有明显的离散性和随机性,内部损伤特征检测是混凝土细观研究的重要内容.针对已有混凝土结构内部损伤特征检测模型精度低的问题,提出一种特征共享双头Cascade R-CNN模型对混凝土CT图像的损伤特征进行检测.首先,...混凝土内部损伤破坏形态具有明显的离散性和随机性,内部损伤特征检测是混凝土细观研究的重要内容.针对已有混凝土结构内部损伤特征检测模型精度低的问题,提出一种特征共享双头Cascade R-CNN模型对混凝土CT图像的损伤特征进行检测.首先,为了有效识别损伤特征的空间信息,构建具有空间敏感性的fc-head(fully connected head)与空间相关性的conv-head(convolution head)相结合的Cascade R-CNN网络模型;其次,通过特征共享的方法将检测网络各层级分类信息进行融合,提升低IOU(intersection over union)阈值(0.5~0.7) ROI (regions of interest)检测任务的精度.实验结果表明,所提方法在检测混凝土CT图像的损伤特征中平均精度达到91.31%,比原始的Cascade R-CNN提高3.04%,低IOU阈值(0.5~0.7) ROI平均精度提高1.49%,该模型可以较好地从混凝土CT图像中检测出细观损伤部分,具有精度高、运算简单、易于工程实现等特点.展开更多
基金partially supported by the National Key Research and Development Program of the Ministry of Science and Technology of China(2022YFE0114200)the National Natural Science Foundation of China(U20A6004).
文摘This paper investigates a distributed heterogeneous hybrid blocking flow-shop scheduling problem(DHHBFSP)designed to minimize the total tardiness and total energy consumption simultaneously,and proposes an improved proximal policy optimization(IPPO)method to make real-time decisions for the DHHBFSP.A multi-objective Markov decision process is modeled for the DHHBFSP,where the reward function is represented by a vector with dynamic weights instead of the common objectiverelated scalar value.A factory agent(FA)is formulated for each factory to select unscheduled jobs and is trained by the proposed IPPO to improve the decision quality.Multiple FAs work asynchronously to allocate jobs that arrive randomly at the shop.A two-stage training strategy is introduced in the IPPO,which learns from both single-and dual-policy data for better data utilization.The proposed IPPO is tested on randomly generated instances and compared with variants of the basic proximal policy optimization(PPO),dispatch rules,multi-objective metaheuristics,and multi-agent reinforcement learning methods.Extensive experimental results suggest that the proposed strategies offer significant improvements to the basic PPO,and the proposed IPPO outperforms the state-of-the-art scheduling methods in both convergence and solution quality.
基金the Shanghai Pujiang Program (No.22PJD030),the National Natural Science Foundation of China (Nos.61603244 and 71904116)the National Natural Science Foundation of China-Shandong Joint Fund (No.U2006228)。
文摘The overall performance of multi-robot collaborative systems is significantly affected by the multi-robot task allocation.To improve the effectiveness,robustness,and safety of multi-robot collaborative systems,a multimodal multi-objective evolutionary algorithm based on deep reinforcement learning is proposed in this paper.The improved multimodal multi-objective evolutionary algorithm is used to solve multi-robot task allo-cation problems.Moreover,a deep reinforcement learning strategy is used in the last generation to provide a high-quality path for each assigned robot via an end-to-end manner.Comparisons with three popular multimodal multi-objective evolutionary algorithms on three different scenarios of multi-robot task allocation problems are carried out to verify the performance of the proposed algorithm.The experimental test results show that the proposed algorithm can generate sufficient equivalent schemes to improve the availability and robustness of multi-robot collaborative systems in uncertain environments,and also produce the best scheme to improve the overall task execution efficiency of multi-robot collaborative systems.
基金the National Natural Science Foundation of China(62076225,62073300)the Natural Science Foundation for Distinguished Young Scholars of Hubei(2019CFA081)。
文摘Solving constrained multi-objective optimization problems with evolutionary algorithms has attracted considerable attention.Various constrained multi-objective optimization evolutionary algorithms(CMOEAs)have been developed with the use of different algorithmic strategies,evolutionary operators,and constraint-handling techniques.The performance of CMOEAs may be heavily dependent on the operators used,however,it is usually difficult to select suitable operators for the problem at hand.Hence,improving operator selection is promising and necessary for CMOEAs.This work proposes an online operator selection framework assisted by Deep Reinforcement Learning.The dynamics of the population,including convergence,diversity,and feasibility,are regarded as the state;the candidate operators are considered as actions;and the improvement of the population state is treated as the reward.By using a Q-network to learn a policy to estimate the Q-values of all actions,the proposed approach can adaptively select an operator that maximizes the improvement of the population according to the current state and thereby improve the algorithmic performance.The framework is embedded into four popular CMOEAs and assessed on 42 benchmark problems.The experimental results reveal that the proposed Deep Reinforcement Learning-assisted operator selection significantly improves the performance of these CMOEAs and the resulting algorithm obtains better versatility compared to nine state-of-the-art CMOEAs.
基金supported by the National Key Research and Development Program of China under No. 2019YFB1803200。
文摘Resource allocation is an important problem influencing the service quality of multi-beam satellite communications.In multi-beam satellite communications, the available frequency bandwidth is limited, users requirements vary rapidly, high service quality and joint allocation of multi-dimensional resources such as time and frequency are required. It is a difficult problem needs to be researched urgently for multi-beam satellite communications, how to obtain a higher comprehensive utilization rate of multidimensional resources, maximize the number of users and system throughput, and meet the demand of rapid allocation adapting dynamic changed the number of users under the condition of limited resources, with using an efficient and fast resource allocation algorithm.In order to solve the multi-dimensional resource allocation problem of multi-beam satellite communications, this paper establishes a multi-objective optimization model based on the maximum the number of users and system throughput joint optimization goal, and proposes a multi-objective deep reinforcement learning based time-frequency two-dimensional resource allocation(MODRL-TF) algorithm to adapt dynamic changed the number of users and the timeliness requirements. Simulation results show that the proposed algorithm could provide higher comprehensive utilization rate of multi-dimensional resources,and could achieve multi-objective joint optimization,and could obtain better timeliness than traditional heuristic algorithms, such as genetic algorithm(GA)and ant colony optimization algorithm(ACO).
文摘The overall healthcare system has been prioritized within development top lists worldwide.Since many national populations are aging,combined with the availability of sophisticated medical treatments,healthcare expenditures are rapidly growing.Blood banks are a major component of any healthcare system,which store and provide the blood products needed for organ transplants,emergency medical treatments,and routine surgeries.Timely delivery of blood products is vital,especially in emergency settings.Hence,blood delivery process parameters such as safety and speed have received attention in the literature,as well as other parameters such as delivery cost.In this paper,delivery time and cost are modeled mathematically and marked as objective functions requiring simultaneous optimization.A solution is proposed based on Deep Reinforcement Learning(DRL)to address the formulated delivery functions as Multi-objective Optimization Problems(MOPs).The basic concept of the solution is to decompose the MOP into a scalar optimization sub-problems set,where each one of these sub-problems is modeled as a separate Neural Network(NN).The overall model parameters for each sub-problem are optimized based on a neighborhood parameter transfer and DRL training algorithm.The optimization step for the subproblems is undertaken collaboratively to optimize the overall model.Paretooptimal solutions can be directly obtained using the trained NN.Specifically,the multi-objective blood bank delivery problem is addressed in this research.Onemajor technical advantage of this approach is that once the trainedmodel is available,it can be scaled without the need formodel retraining.The scoring can be obtained directly using a straightforward computation of the NN layers in a limited time.The proposed technique provides a set of technical strength points such as the ability to generalize and solve rapidly compared to othermulti-objective optimizationmethods.The model was trained and tested on 5 major hospitals in Saudi Arabia’s Riyadh region,and the simulation results indicated that time and cost decreased by 35%and 30%,respectively.In particular,the proposed model outperformed other state-of-the-art MOP solutions such as Genetic Algorithms and Simulated Annealing.
基金supported by the National Natural Science Foundation of China under Grant 52162050.
文摘This paper presents an improved virtual coupling train set(VCTS)operation control framework to deal with the lack of opti-mization of speed curves in the traditional techniques.The framework takes into account the temporary speed limit on the railway line and the communication delay between trains,and it uses a VCTS consisting of three trains as an experimental object.It creates the virtual coupling train tracking and control process by improving the driving strategy of the leader train and using the leader-follower model.The follower train uses the improved speed curve of the leader train as its speed refer-ence curve through knowledge migration,and this completes the multi-objective optimization of the driving strategy for the VCTS.The experimental results confirm that the deep reinforcement learning algorithm effectively achieves the optimization goal of the train driving strategy.They also reveal that the intrinsic curiosity module prioritized experience replay dueling double deep Q-network(ICM-PER-D3QN)algorithm outperforms the deep Q-network(DQN)algorithm in optimizing the driving strategy of the leader train.The ICM-PER-D3QN algorithm enhances the leader train driving strategy by an average of 57%when compared to the DQN algorithm.Furthermore,the particle swarm optimization(PSO)-based model predictive control(MPC)algorithm has also demonstrated tracking accuracy and further improved safety during VCTS operation,with an average increase of 37.7%in tracking accuracy compared to the traditional MPC algorithm.
文摘This research paper presents a comprehensive investigation into the effectiveness of the DeepSurNet-NSGA II(Deep Surrogate Model-Assisted Non-dominated Sorting Genetic Algorithm II)for solving complex multiobjective optimization problems,with a particular focus on robotic leg-linkage design.The study introduces an innovative approach that integrates deep learning-based surrogate models with the robust Non-dominated Sorting Genetic Algorithm II,aiming to enhance the efficiency and precision of the optimization process.Through a series of empirical experiments and algorithmic analyses,the paper demonstrates a high degree of correlation between solutions generated by the DeepSurNet-NSGA II and those obtained from direct experimental methods,underscoring the algorithm’s capability to accurately approximate the Pareto-optimal frontier while significantly reducing computational demands.The methodology encompasses a detailed exploration of the algorithm’s configuration,the experimental setup,and the criteria for performance evaluation,ensuring the reproducibility of results and facilitating future advancements in the field.The findings of this study not only confirm the practical applicability and theoretical soundness of the DeepSurNet-NSGA II in navigating the intricacies of multi-objective optimization but also highlight its potential as a transformative tool in engineering and design optimization.By bridging the gap between complex optimization challenges and achievable solutions,this research contributes valuable insights into the optimization domain,offering a promising direction for future inquiries and technological innovations.
基金supported in part by the National Key Research Project of China under Grant No.2023YFA1009402General Science and Technology Plan Items in Zhejiang Province ZJKJT-2023-02.
文摘With the remarkable advancements in machine vision research and its ever-expanding applications,scholars have increasingly focused on harnessing various vision methodologies within the industrial realm.Specifically,detecting vehicle floor welding points poses unique challenges,including high operational costs and limited portability in practical settings.To address these challenges,this paper innovatively integrates template matching and the Faster RCNN algorithm,presenting an industrial fusion cascaded solder joint detection algorithm that seamlessly blends template matching with deep learning techniques.This algorithm meticulously weights and fuses the optimized features of both methodologies,enhancing the overall detection capabilities.Furthermore,it introduces an optimized multi-scale and multi-template matching approach,leveraging a diverse array of templates and image pyramid algorithms to bolster the accuracy and resilience of object detection.By integrating deep learning algorithms with this multi-scale and multi-template matching strategy,the cascaded target matching algorithm effectively accurately identifies solder joint types and positions.A comprehensive welding point dataset,labeled by experts specifically for vehicle detection,was constructed based on images from authentic industrial environments to validate the algorithm’s performance.Experiments demonstrate the algorithm’s compelling performance in industrial scenarios,outperforming the single-template matching algorithm by 21.3%,the multi-scale and multitemplate matching algorithm by 3.4%,the Faster RCNN algorithm by 19.7%,and the YOLOv9 algorithm by 17.3%in terms of solder joint detection accuracy.This optimized algorithm exhibits remarkable robustness and portability,ideally suited for detecting solder joints across diverse vehicle workpieces.Notably,this study’s dataset and feature fusion approach can be a valuable resource for other algorithms seeking to enhance their solder joint detection capabilities.This work thus not only presents a novel and effective solution for industrial solder joint detection but lays the groundwork for future advancements in this critical area.
基金financially supported by the National Natural Science Foundation of China(No.52004029)the Fundamental Research Funds for the Central Universities,China(No.FRF-TT-20-06).
文摘Higher requirements for the accuracy of relevant models are put throughout the transformation and upgrade of the iron and steel sector to intelligent production.It has been difficult to meet the needs of the field with the usual prediction model of mechanical properties of hotrolled strip.Insufficient data and difficult parameter adjustment limit deep learning models based on multi-layer networks in practical applications;besides,the limited discrete process parameters used make it impossible to effectively depict the actual strip processing process.In order to solve these problems,this research proposed a new sampling approach for mechanical characteristics input data of hot-rolled strip based on the multi-grained cascade forest(gcForest)framework.According to the characteristics of complex process flow and abnormal sensitivity of process path and parameters to product quality in the hot-rolled strip production,a three-dimensional continuous time series process data sampling method based on time-temperature-deformation was designed.The basic information of strip steel(chemical composition and typical process parameters)is fused with the local process information collected by multi-grained scanning,so that the next link’s input has both local and global features.Furthermore,in the multi-grained scanning structure,a sub sampling scheme with a variable window was designed,so that input data with different dimensions can get output characteristics of the same dimension after passing through the multi-grained scanning structure,allowing the cascade forest structure to be trained normally.Finally,actual production data of three steel grades was used to conduct the experimental evaluation.The results revealed that the gcForest-based mechanical property prediction model outperforms the competition in terms of comprehensive performance,ease of parameter adjustment,and ability to sustain high prediction accuracy with fewer samples.
基金supported by Key Science and Technology Program of Henan Province,China(Grant Nos.242102210147,242102210027)Fujian Province Young and Middle aged Teacher Education Research Project(Science and Technology Category)(No.JZ240101)(Corresponding author:Dong Yuan).
文摘Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit(RSU),thereby achieving lower delay and energy consumption.However,due to the limited storage capacity and energy budget of RSUs,it is challenging to meet the demands of the highly dynamic Internet of Vehicles(IoV)environment.Therefore,determining reasonable service caching and computation offloading strategies is crucial.To address this,this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading.By modeling the dynamic optimization problem using Markov Decision Processes(MDP),the scheme jointly optimizes task delay,energy consumption,load balancing,and privacy entropy to achieve better quality of service.Additionally,a dynamic adaptive multi-objective deep reinforcement learning algorithm is proposed.Each Double Deep Q-Network(DDQN)agent obtains rewards for different objectives based on distinct reward functions and dynamically updates the objective weights by learning the value changes between objectives using Radial Basis Function Networks(RBFN),thereby efficiently approximating the Pareto-optimal decisions for multiple objectives.Extensive experiments demonstrate that the proposed algorithm can better coordinate the three-tier computing resources of cloud,edge,and vehicles.Compared to existing algorithms,the proposed method reduces task delay and energy consumption by 10.64%and 5.1%,respectively.
文摘Several applications of machine learning and artificial intelligence,have acquired importance and come to the fore as a result of recent advances and improvements in these approaches.Autonomous cars are one such application.This is expected to have a significant and revolutionary influence on society.Integration with smart cities,new infrastructure and urban planning with sophisticated cyber-security are some of the current ramifications of self-driving automobiles.The autonomous automobile,often known as selfdriving systems or driverless vehicles,is a vehicle that can perceive its surroundings and navigate predetermined routes without human involvement.Cars are on the verge of evolving into autonomous robots,thanks to significant breakthroughs in artificial intelligence and related technologies,and this will have a wide range of socio-economic implications.However,in order for these automobiles to become a reality,they must be endowed with the perception and cognition necessary to deal with high-pressure real-life events and make proper judgments and take appropriate action.The majority of self-driving car technologies are based on computer systems that automate vehicle control parts.From forward-collision warning and antilock brakes to lane-keeping and adaptive drive control,to fully automated driving,these technological components have a wide range of capabilities.A self-driving car combines a wide range of sensors,actuators,and cameras.Recent researches on computer vision and deep learning are used to control autonomous driving systems.For self-driving automobiles,lane-keeping is crucial.This study presents a deep learning approach to obtain the proper steering angle to maintain the robot in the lane.We propose an advanced control for a selfdriving robot by using two controllers simultaneously.Convolutional neural networks(CNNs)are employed,to predict the car’and a proportionalintegral-derivative(PID)controller is designed for speed and steering control.This study uses a Raspberry PI based camera to control the robot car.
文摘Deep deterministic policy gradient(DDPG)has been proved to be effective in optimizing particle swarm optimization(PSO),but whether DDPG can optimize multi-objective discrete particle swarm optimization(MODPSO)remains to be determined.The present work aims to probe into this topic.Experiments showed that the DDPG can not only quickly improve the convergence speed of MODPSO,but also overcome the problem of local optimal solution that MODPSO may suffer.The research findings are of great significance for the theoretical research and application of MODPSO.
基金the support of the National Natural Science Foundation of China(No.52106053,No.92152301)。
文摘For complex flows in compressors containing flow separations and adverse pressure gradients,the numerical simulation results based on Reynolds-averaged Navier-Stokes(RANS)models often deviate from experimental measurements more or less.To improve the prediction accuracy and reduce the difference between the RANS prediction results and experimental measurements,an experimental data-driven flow field prediction method based on deep learning and l_(1)regularization is proposed and applied to a compressor cascade flow field.The inlet boundary conditions and turbulence model parameters are calibrated to obtain the high-fidelity flow fields.The Saplart-Allmaras and SST turbulence models are used independently for mutual validation.The contributions of key modified parameters are also analyzed via sensitivity analysis.The results show that the prediction error can be reduced by nearly 70%based on the proposed algorithm.The flow fields predicted by the two calibrated turbulence models are almost the same and nearly independent of the turbulence models.The corrections of the inlet boundary conditions reduce the error in the first half of the chord.The turbulence model calibrations fix the overprediction of flow separation on the suction surface near the tail edge.
文摘混凝土内部损伤破坏形态具有明显的离散性和随机性,内部损伤特征检测是混凝土细观研究的重要内容.针对已有混凝土结构内部损伤特征检测模型精度低的问题,提出一种特征共享双头Cascade R-CNN模型对混凝土CT图像的损伤特征进行检测.首先,为了有效识别损伤特征的空间信息,构建具有空间敏感性的fc-head(fully connected head)与空间相关性的conv-head(convolution head)相结合的Cascade R-CNN网络模型;其次,通过特征共享的方法将检测网络各层级分类信息进行融合,提升低IOU(intersection over union)阈值(0.5~0.7) ROI (regions of interest)检测任务的精度.实验结果表明,所提方法在检测混凝土CT图像的损伤特征中平均精度达到91.31%,比原始的Cascade R-CNN提高3.04%,低IOU阈值(0.5~0.7) ROI平均精度提高1.49%,该模型可以较好地从混凝土CT图像中检测出细观损伤部分,具有精度高、运算简单、易于工程实现等特点.
