Artificial bee colony(ABC) is one of the most popular swarm intelligence optimization algorithms which have been widely used in numerical optimization and engineering applications. However, there are still deficiencie...Artificial bee colony(ABC) is one of the most popular swarm intelligence optimization algorithms which have been widely used in numerical optimization and engineering applications. However, there are still deficiencies in ABC regarding its local search ability and global search efficiency. Aiming at these deficiencies,an ABC variant named hybrid ABC(HABC) algorithm is proposed.Firstly, the variable neighborhood search factor is added to the solution search equation, which can enhance the local search ability and increase the population diversity. Secondly, inspired by the neuroscience investigation of real honeybees, the memory mechanism is put forward, which assumes the artificial bees can remember their past successful experiences and further guide the subsequent foraging behavior. The proposed memory mechanism is used to improve the global search efficiency. Finally, the results of comparison on a set of ten benchmark functions demonstrate the superiority of HABC.展开更多
A self-adaptive large neighborhood search method for scheduling n jobs on m non-identical parallel machines with mul- tiple time windows is presented. The problems＇ another feature lies in oversubscription, namely no...A self-adaptive large neighborhood search method for scheduling n jobs on m non-identical parallel machines with mul- tiple time windows is presented. The problems＇ another feature lies in oversubscription, namely not all jobs can be scheduled within specified scheduling horizons due to the limited machine capacity. The objective is thus to maximize the overall profits of processed jobs while respecting machine constraints. A first-in- first-out heuristic is applied to find an initial solution, and then a large neighborhood search procedure is employed to relax and re- optimize cumbersome solutions. A machine learning mechanism is also introduced to converge on the most efficient neighborhoods for the problem. Extensive computational results are presented based on data from an application involving the daily observation scheduling of a fleet of earth observing satellites. The method rapidly solves most problem instances to optimal or near optimal and shows a robust performance in sensitive analysis.展开更多
This paper presents a neighborhood optimal trajectory online correction algorithm considering terminal time variation,and investigates its application range.Firstly,the motion model of midcourse guidance is establishe...This paper presents a neighborhood optimal trajectory online correction algorithm considering terminal time variation,and investigates its application range.Firstly,the motion model of midcourse guidance is established,and the online trajectory correction-regenerating strategy is introduced.Secondly,based on the neighborhood optimal control theory,a neighborhood optimal trajectory online correction algorithm considering the terminal time variation is proposed by adding the consideration of terminal time variation to the traditional neighborhood optimal trajectory correction method.Thirdly,the Monte Carlo simulation method is used to analyze the application range of the algorithm,which provides a basis for the division of application domain of the online correction algorithm and the online regeneration algorithm of midcourse guidance trajectory.Finally,the simulation results show that the algorithm has high real-time performance,and the online correction trajectory can meet the requirements of terminal constraint change.The application range of the algorithm is obtained through Monte Carlo simulation.展开更多
The neighborhood center provides an implementation case for centralizing community service facilities and organizing service provision effectively,and has achieved community service improvement,which provides innovati...The neighborhood center provides an implementation case for centralizing community service facilities and organizing service provision effectively,and has achieved community service improvement,which provides innovative methods for governance innovation.This paper summarizes the development process of neighborhood center planning in China,and analyzes related planning cases in Suzhou,Tianjin,Hangzhou,and Chengdu focusing on planning characteristics and operating mechanisms.Based on the case of Chongqing,it gives answer to key issues of the local application of neighborhood center planning concept.展开更多
针对基于自主移动机器人(Autonomous Mobile Robot,AMR)的货到人拣选系统多拣货台场景,研究订单分配、处理顺序及货架访问顺序的集成优化,提出多拣货台订单分配与排序问题(Order Allocation and Sequencing Problem,OASP),对订单如何分...针对基于自主移动机器人(Autonomous Mobile Robot,AMR)的货到人拣选系统多拣货台场景,研究订单分配、处理顺序及货架访问顺序的集成优化,提出多拣货台订单分配与排序问题(Order Allocation and Sequencing Problem,OASP),对订单如何分配给拣货台、订单在拣货台的处理顺序及如何安排货架的访问顺序进行集成优化决策,并以最小化订单拣选时间为目标建立混合整数规划模型.设计变邻域搜索算法(the Variable Neighborhood Search Algorithm,VNSA),通过订单相似度进行分批分配并生成贪婪初始解,结合货架置换、订单重分配的抖动算子和订单交换/插入、货架序列调整等4种局部优化邻域,采用动态切换机制实现迭代寻优,并将设计的算法与CPLEX求解器进行比较.研究结果表明:VNSA算法在小规模算例中求解速度与精度优于CPLEX求解器;在大规模算例中对初始解的优化能力显著,验证了联合优化订单分配和排序的有效性;订单拣选时间与拣货台数量、容量呈负相关,与负载平衡系数呈正相关.展开更多
阿尔茨海默病(Alzheimer’s Disease,AD)是一种慢性神经系统退行性疾病,其准确分类有助于实现AD的早期诊断,从而及时采取针对性的治疗和干预措施.本文提出了一种最近邻域聚合图神经网络(Graph neural network with nearest Neighborhood...阿尔茨海默病(Alzheimer’s Disease,AD)是一种慢性神经系统退行性疾病,其准确分类有助于实现AD的早期诊断,从而及时采取针对性的治疗和干预措施.本文提出了一种最近邻域聚合图神经网络(Graph neural network with nearest Neighborhood AgGrEgation,GraphNAGE)的AD分类新方法.首先进行图数据建模,将AD数据样本表示为图数据.采用基于互信息(Mutual Information,MI)的特征选择方法,从样本的114维大脑皮层与皮层下感兴趣区域(Cerebral Cortex and Subcortical Regions Of Interest,CCS-ROI)的体积特征中选取重要性高的体积特征,并将其用于节点建模.提出基于相似性度量的关系建模方法,利用重要性高的体积特征、遗传基因、人口统计信息和认知评分对样本之间的关系进行建模.进而构建GraphNAGE,针对每个节点,基于与该节点相关的边的权重进行最近邻域采样,然后使用均值聚合方法对采样得到的邻居节点和中心节点的数据进行聚合,最后通过一个全连接层和一个Softmax层实现AD分类.在TADPOLE(The Alzheimer’s Disease Prediction Of Longitudinal Evolution)数据集上进行实验,结果表明:本文提出的AD分类方法的准确率(ACCuracy,ACC)为98.20%,F_(1)分数为97.34%,曲线下面积(Area Under Curve,AUC)为97.80%.实验结果表明:本文提出的AD分类方法充分利用了AD数据样本之间的相关性,其性能优于传统的基于机器学习、深度学习和图神经网络(Graph Neural Network,GNN)的AD分类方法.展开更多
基金supported by the National Natural Science Foundation of China(7177121671701209)
文摘Artificial bee colony(ABC) is one of the most popular swarm intelligence optimization algorithms which have been widely used in numerical optimization and engineering applications. However, there are still deficiencies in ABC regarding its local search ability and global search efficiency. Aiming at these deficiencies,an ABC variant named hybrid ABC(HABC) algorithm is proposed.Firstly, the variable neighborhood search factor is added to the solution search equation, which can enhance the local search ability and increase the population diversity. Secondly, inspired by the neuroscience investigation of real honeybees, the memory mechanism is put forward, which assumes the artificial bees can remember their past successful experiences and further guide the subsequent foraging behavior. The proposed memory mechanism is used to improve the global search efficiency. Finally, the results of comparison on a set of ten benchmark functions demonstrate the superiority of HABC.
基金supported by the National Natural Science Foundation of China (7060103570801062)
文摘A self-adaptive large neighborhood search method for scheduling n jobs on m non-identical parallel machines with mul- tiple time windows is presented. The problems＇ another feature lies in oversubscription, namely not all jobs can be scheduled within specified scheduling horizons due to the limited machine capacity. The objective is thus to maximize the overall profits of processed jobs while respecting machine constraints. A first-in- first-out heuristic is applied to find an initial solution, and then a large neighborhood search procedure is employed to relax and re- optimize cumbersome solutions. A machine learning mechanism is also introduced to converge on the most efficient neighborhoods for the problem. Extensive computational results are presented based on data from an application involving the daily observation scheduling of a fleet of earth observing satellites. The method rapidly solves most problem instances to optimal or near optimal and shows a robust performance in sensitive analysis.
基金supported by the National Natural Science Foundation of China(61873278,62173339)。
文摘This paper presents a neighborhood optimal trajectory online correction algorithm considering terminal time variation,and investigates its application range.Firstly,the motion model of midcourse guidance is established,and the online trajectory correction-regenerating strategy is introduced.Secondly,based on the neighborhood optimal control theory,a neighborhood optimal trajectory online correction algorithm considering the terminal time variation is proposed by adding the consideration of terminal time variation to the traditional neighborhood optimal trajectory correction method.Thirdly,the Monte Carlo simulation method is used to analyze the application range of the algorithm,which provides a basis for the division of application domain of the online correction algorithm and the online regeneration algorithm of midcourse guidance trajectory.Finally,the simulation results show that the algorithm has high real-time performance,and the online correction trajectory can meet the requirements of terminal constraint change.The application range of the algorithm is obtained through Monte Carlo simulation.
基金based on the project of“The Study of Neighborhood Center Planning in Cuiyun District,Yubei District,Chongqing(2018)”,whose research work is undertaken by Chongqing Universitysupported by the National Natural Science Foundation of China(Grant No.51778078)
文摘The neighborhood center provides an implementation case for centralizing community service facilities and organizing service provision effectively,and has achieved community service improvement,which provides innovative methods for governance innovation.This paper summarizes the development process of neighborhood center planning in China,and analyzes related planning cases in Suzhou,Tianjin,Hangzhou,and Chengdu focusing on planning characteristics and operating mechanisms.Based on the case of Chongqing,it gives answer to key issues of the local application of neighborhood center planning concept.
文摘阿尔茨海默病(Alzheimer’s Disease,AD)是一种慢性神经系统退行性疾病,其准确分类有助于实现AD的早期诊断,从而及时采取针对性的治疗和干预措施.本文提出了一种最近邻域聚合图神经网络(Graph neural network with nearest Neighborhood AgGrEgation,GraphNAGE)的AD分类新方法.首先进行图数据建模,将AD数据样本表示为图数据.采用基于互信息(Mutual Information,MI)的特征选择方法,从样本的114维大脑皮层与皮层下感兴趣区域(Cerebral Cortex and Subcortical Regions Of Interest,CCS-ROI)的体积特征中选取重要性高的体积特征,并将其用于节点建模.提出基于相似性度量的关系建模方法,利用重要性高的体积特征、遗传基因、人口统计信息和认知评分对样本之间的关系进行建模.进而构建GraphNAGE,针对每个节点,基于与该节点相关的边的权重进行最近邻域采样,然后使用均值聚合方法对采样得到的邻居节点和中心节点的数据进行聚合,最后通过一个全连接层和一个Softmax层实现AD分类.在TADPOLE(The Alzheimer’s Disease Prediction Of Longitudinal Evolution)数据集上进行实验,结果表明:本文提出的AD分类方法的准确率(ACCuracy,ACC)为98.20%,F_(1)分数为97.34%,曲线下面积(Area Under Curve,AUC)为97.80%.实验结果表明:本文提出的AD分类方法充分利用了AD数据样本之间的相关性,其性能优于传统的基于机器学习、深度学习和图神经网络(Graph Neural Network,GNN)的AD分类方法.
