考虑Pareto最优的列车运行图与维修天窗协调优化

列车运行图与维修天窗之间的冲突始终无法避免,且维修天窗开设时间的长短显著影响列车总运行时间。针对此问题,综合考虑维修天窗对列车运行造成的限速约束、车站到发线数量约束等,建立列车总运行时间最小,以及维修天窗实际开设时长与理想时长总偏差最小的双目标混合整数规划模型;对困难约束设置中间辅助变量将模型线性化以提高求解效率,并设计约束转换算法求解双目标模型的Pareto最优;微观化处理铁路线,将站内资源和站间资源细化为一系列行车资源单元,得到更加符合实际旅客运输需求的运行图。以某地区铁路线夜间开行列车及维修天窗开设计划为研究背景,调用商业软件求解双目标函数模型的Pareto最优,并对双目标模型的最小支配解和最优支配解进行对比分析;针对最优支配解下的列车进入、离开行车资源单元的时间、停站作业时间及维修天窗的开始时间及开设时长,绘制列车运行图。求解结果表明:模型在满足维修天窗最小开设时长的同时,能够兼顾列车运行总时间最小和维修天窗开设时长更充裕。基于最优支配解绘制的列车运行图表明:微观路网下的列车运行时刻表优化结果更符合实际旅客运输生产作业需要。研究结果可为铁路运营管理部门进一步优化列车运行图编制与维修天窗开设提供参考。

基于Pareto蚁群算法的双目标路径规划研究

针对复杂建筑环境人员应急疏散单一路径不能满足火灾环境变化需求的问题,基于改进蚁群算法,结合Pareto双目标解集思想,提出一种组合优化解集的双目标蚁群算法,通过排序优化的思想,实现人员多路径动态疏散规划。在构造Pareto解集的阶段协同考虑疏散路径长度以及火灾风险程度2个优化目标,计算各个解之间的支配关系。利用排序优化蚁群算法的正反馈机制将各组解的信息素按一定比例作为最优路径信息素的积累,加快解集的寻找。最后将其与传统双目标蚁群算法相比较,结果表明:优化后的双目标算法更加适合复杂建筑人员疏散路径规划问题,在寻找多组满足要求解的同时展示目标之间的利弊关系,供决策者选择合适的路径,提高疏散效率。

面向供应链分销的多维空间Pareto边界自动谈判模型研究

随着电子商务的快速发展,自动谈判逐渐成为提升供应链系统效率的一种手段。为了优化多方参与的供应链分销谈判应用,本文将多边多属性谈判问题转化为多目标优化模型,采用改进的非支配遗传算法NSGA-Ⅲ计算多维空间的Pareto边界;然后,设计多线程谈判模型,将参与多方谈判的买卖各方拆解为多个双边谈判线程,分别在多维Pareto边界上进行谈判;进而,采用动态时间依赖策略(DTD),使Agent根据对方报价在Pareto边界上动态调整让步策略,快速达成协议。为验证模型的有效性,本文进行了大量模拟自动谈判实验。实验结果表明,所提出的改进算法和谈判流程优于领域最新研究成果,能有效提升多边多属性谈判效率,有助于多方达成共赢局面。

Pareto解集旋转的分类多策略预测动态多目标优化

为更有效地解决Pareto解集(Pareto set,PS)旋转的动态多目标优化问题,提出PS旋转的分类多策略预测方法(rotation-based forecasting method,RFM)。将PS的旋转类型分为PS中心点旋转、PS原点旋转和非标准旋转;针对以上不同的PS旋转类型,自适应地选择合适的预测模型,建立不同点集的时间序列,为后续进化提供初始种群。引入拉丁超立方策略(Latin hypercube strategy,LHS)生成的随机种群与上述预测种群共同构建新种群,保证种群的多样性。为验证算法的有效性,将RFM算法与DNSGA-II、PPS、SPPS和MMP算法在8个标准的动态测试函数上进行实验对比。实验结果表明,RFM算法取得了6个最优IGD值、7个最优SP值、3个最优MS值,证明了RFM算法可以更有效地解决基于PS旋转的动态多目标优化问题。同时验证了RFM算法的普适性,在FDA系列函数上进行实验对比,实验结果表明,该算法在处理非旋转的动态多目标优化问题中仍具有较优性能。

双边定时截尾下Pareto分布的参数的极大似然估计的EM算法

给出了当寿命分布为Pareto分布时,双边定时截尾寿命试验下形状参数的极大似然估计。由于似然方程形式较复杂,无法得到参数的显式表达式。但可证明此极大似然估计是唯一存在的,并利用EM算法求出了此参数的一种估计。

Pareto法分析66例急性中毒住院儿童病例特点及药学建议

目的通过分析急性中毒住院儿童病例,总结病例特点及规律,为预防和减少儿童急性中毒提供科学依据。方法采用帕累托图(Pareto)法回顾性统计分析医院因急性中毒而收治住院的66例儿童病例资料,对患儿年龄分布、中毒季节、引起患儿中毒的品种分布、药品种类分布、剂型分布特点进行统计分析。结果儿童急性中毒年龄、季节、中毒品种、中毒药物剂型的主要因素有:1~3岁,第二、三季度,药品,片剂。中毒药品的主要因素为中枢神经系统用药、心血管系统用药、呼吸系统用药、影响变态反应药物。中毒药物例次最多的为氯硝西泮片。结论急性中毒以1~3岁儿童为主;主要发生在第二、三季度;以药品为主,剂型以片剂为主;国家有关部门应进一步完善相关立法、推广儿童药品安全包装、控制处方限量等源头控制;通过增强科普教育及监护人安全意识,进而有效降低儿童急性中毒的发生率。

基于Pareto-GA多目标的企业管理系统优化研究——以某造纸厂为例

传统造纸厂管理优化通常只针对单一目标,忽略了质量和安全等重要方面。因此,提出了一种面向造纸厂的多个目标优化方法,并结合Pareto排序以及遗传算法搜索机制改进的Pareto-遗传算法作为求解方法,以实现对造纸厂监管系统的优化设计。研究结果显示,使用Schaffer's F6 Function进行测试时,改进的Pareto-遗传算法在72次迭代后达到最大适应度值0.93,优于其他两种算法。进一步将工期、成本、质量和安全多目标问题分解为两个子问题,成功获得3组Pareto最优解,为管理者提供不同需求下的优化方案。同时,提出的造纸厂管理系统优化设计方案能够提升造纸厂管理的效率和安全性,具有重要的理论价值和实际应用前景。

由Chvátal定理引出的关于Weibull分布和Pareto分布的研究

受Chvátal定理的启发,本文研究了随机变量不超过其期望的概率的下确界问题.利用分析的方法,我们得到了当随机变量的分布为Weibull分布或Pareto分布时该随机变量不超过其期望的概率的下确界.

基于Pareto控制的多目标PSO算法在铣削参数优化中的应用

工艺参数是影响零件加工质量和效率的关键因素,工艺参数的优化和调节是改善加工工艺的最有效方法。针对铣削加工参数优化问题,提出了一种基于应用实例的多目标切削参数优化方法。首先,以材料去除率、切削力和刀具寿命为目标函数,建立了统一的切削工艺参数多目标优化数学模型。随后,使用切削数据对目标函数进行组合,建立了该问题的数学模型,研究了适用的求解方法以获得最优解,并通过实验验证了参数优化的有效性。该方法可为加工参数的选择提供指导和依据。

极值服从广义Pareto分布的扭转载荷外推方法研究

提出了一种极值样本服从广义Pareto分布(generalized pareto distribution,GPD)函数的扭转载荷时域外推方法,基于极值样本的均值超出函数,首先确定一个区间范围,并以形状参数最小的均方误差为目标,通过自抽样法确定最佳阈值,再采用极大似然估计法对GPD函数的形状参数和尺度参数进行估计,获取扭转随机载荷谱的极值样本,服从GPD分布规律。以商用车驾驶室的稳定杆为研究对象,介绍了扭转载荷采集的方法,基于所提出的扭转载荷时域外推方法进行外推研究,并分别从穿级计数、功率谱密度、雨流图和潜在伪损伤量等方面对外推前后的载荷谱进行了对比分析。结果表明:所构建的载荷外推算法对商用车驾驶室稳定杆扭转载荷外推有较好适应性。

给定边数的超仙人掌图的第二大Pareto H-特征值

设λ_(2)(H)是超图H的第二大Pareto H-特征值,探讨了超仙人掌图的Pareto H-特征值,特别地,通过刻画超仙人掌的极值图,给出了超仙人掌图的第二大Pareto H-特征值的上界并刻画出了该上界所对应的极值超图.

求解多目标流水车间调度Pareto最优解的遗传强化算法

针对多目标流水车间调度Pareto最优问题,本文建立了以最大完工时间和最大拖延时间为优化目标的多目标流水车间调度问题模型,并设计了一种基于Q-learning的遗传强化学习算法求解该问题的Pareto最优解.该算法引入状态变量和动作变量,通过Q-learning算法获得初始种群,以提高初始解质量.在算法进化过程中,利用Q表指导变异操作,扩大局部搜索范围.采用Pareto快速非支配排序以及拥挤度计算提高解的质量以及多样性,逐步获得Pareto最优解.通过与遗传算法、NSGA-II算法和Q-learning算法进行对比实验,验证了改进后的遗传强化算法在求解多目标流水车间调度问题Pareto最优解的有效性.

逐步增加Ⅱ型截尾下Pareto分布形状参数的Bayes估计

基于逐步增加Ⅱ型截尾样本,首先得出Pareto分布形状参数的极大似然估计,考虑两个损失函数和形状参数的两个先验分布,得出该分布形状参数的4个Bayes估计。由数值模拟结果发现,上述四个Bayes估计值的均方误差均小于极大似然估计值,其中,当损失函数为二次损失函数,形状参数的先验分布为共轭先验分布时的Bayes估计的均方误差较小,估计效果更理想,且实例分析与数值模拟结果相符。其次在二次损失函数下,针对形状参数先验分布选取共轭先验分布,给出Pareto分布形状参数的多层Bayes估计和E-Bayes估计。

Applying an Improved Dung Beetle Optimizer Algorithm to Network Traffic Identification

Network traffic identification is critical for maintaining network security and further meeting various demands of network applications.However,network traffic data typically possesses high dimensionality and complexity,leading to practical problems in traffic identification data analytics.Since the original Dung Beetle Optimizer(DBO)algorithm,Grey Wolf Optimization(GWO)algorithm,Whale Optimization Algorithm(WOA),and Particle Swarm Optimization(PSO)algorithm have the shortcomings of slow convergence and easily fall into the local optimal solution,an Improved Dung Beetle Optimizer(IDBO)algorithm is proposed for network traffic identification.Firstly,the Sobol sequence is utilized to initialize the dung beetle population,laying the foundation for finding the global optimal solution.Next,an integration of levy flight and golden sine strategy is suggested to give dung beetles a greater probability of exploring unvisited areas,escaping from the local optimal solution,and converging more effectively towards a global optimal solution.Finally,an adaptive weight factor is utilized to enhance the search capabilities of the original DBO algorithm and accelerate convergence.With the improvements above,the proposed IDBO algorithm is then applied to traffic identification data analytics and feature selection,as so to find the optimal subset for K-Nearest Neighbor(KNN)classification.The simulation experiments use the CICIDS2017 dataset to verify the effectiveness of the proposed IDBO algorithm and compare it with the original DBO,GWO,WOA,and PSO algorithms.The experimental results show that,compared with other algorithms,the accuracy and recall are improved by 1.53%and 0.88%in binary classification,and the Distributed Denial of Service(DDoS)class identification is the most effective in multi-classification,with an improvement of 5.80%and 0.33%for accuracy and recall,respectively.Therefore,the proposed IDBO algorithm is effective in increasing the efficiency of traffic identification and solving the problem of the original DBO algorithm that converges slowly and falls into the local optimal solution when dealing with high-dimensional data analytics and feature selection for network traffic identification.

基于Pareto解集的工业园区微网优化配置研究

工业园区各类能源耦合性较差,不同能源系统独立运行,能源利用率较低,需从多类能源互联集成和互补融合入手,提高能源的综合利用效率和可再生能源的利用率。提出了一种工业园区综合能源微网方案,以总成本、污染治理费、电网可靠性、风光互补性为目标函数和约束条件,建立了多能微电网优化模型。设计了一种基于Pareto最优解集的多目标差分算法并采用熵权法确定每个评价指标的权重,将多目标运算转化为单目标运算。仿真结果表明,该算法在微电网端可有效降低传统能源的消耗,加强清洁能源的有效利用,减少碳排放量并降低系统运行成本。

Urban Traffic Control Meets Decision Recommendation System:A Survey and Perspective

Urban traffic control is a multifaceted and demanding task that necessitates extensive decision-making to ensure the safety and efficiency of urban transportation systems.Traditional approaches require traffic signal professionals to manually intervene on traffic control devices at the intersection level,utilizing their knowledge and expertise.However,this process is cumbersome,labor-intensive,and cannot be applied on a large network scale.Recent studies have begun to explore the applicability of recommendation system for urban traffic control,which offer increased control efficiency and scalability.Such a decision recommendation system is complex,with various interdependent components,but a systematic literature review has not yet been conducted.In this work,we present an up-to-date survey that elucidates all the detailed components of a recommendation system for urban traffic control,demonstrates the utility and efficacy of such a system in the real world using data and knowledgedriven approaches,and discusses the current challenges and potential future directions of this field.

Spatiotemporal Prediction of Urban Traffics Based on Deep GNN

Traffic prediction already plays a significant role in applications like traffic planning and urban management,but it is still difficult to capture the highly non-linear and complicated spatiotemporal correlations of traffic data.As well as to fulfil both long-termand short-termprediction objectives,a better representation of the temporal dependency and global spatial correlation of traffic data is needed.In order to do this,the Spatiotemporal Graph Neural Network(S-GNN)is proposed in this research as amethod for traffic prediction.The S-GNN simultaneously accepts various traffic data as inputs and investigates the non-linear correlations between the variables.In terms of modelling,the road network is initially represented as a spatiotemporal directed graph,with the features of the samples at the time step being captured by a convolution module.In order to assign varying attention weights to various adjacent area nodes of the target node,the adjacent areas information of nodes in the road network is then aggregated using a graph network.The data is output using a fully connected layer at the end.The findings show that S-GNN can improve short-and long-term traffic prediction accuracy to a greater extent;in comparison to the control model,the RMSE of S-GNN is reduced by about 0.571 to 9.288 and the MAE(Mean Absolute Error)by about 0.314 to 7.678.The experimental results on two real datasets,Pe MSD7(M)and PEMS-BAY,also support this claim.

HGNN-ETC: Higher-Order Graph Neural Network Based on Chronological Relationships for Encrypted Traffic Classification

Encrypted traffic plays a crucial role in safeguarding network security and user privacy.However,encrypting malicious traffic can lead to numerous security issues,making the effective classification of encrypted traffic essential.Existing methods for detecting encrypted traffic face two significant challenges.First,relying solely on the original byte information for classification fails to leverage the rich temporal relationships within network traffic.Second,machine learning and convolutional neural network methods lack sufficient network expression capabilities,hindering the full exploration of traffic’s potential characteristics.To address these limitations,this study introduces a traffic classification method that utilizes time relationships and a higher-order graph neural network,termed HGNN-ETC.This approach fully exploits the original byte information and chronological relationships of traffic packets,transforming traffic data into a graph structure to provide the model with more comprehensive context information.HGNN-ETC employs an innovative k-dimensional graph neural network to effectively capture the multi-scale structural features of traffic graphs,enabling more accurate classification.We select the ISCXVPN and the USTC-TK2016 dataset for our experiments.The results show that compared with other state-of-the-art methods,our method can obtain a better classification effect on different datasets,and the accuracy rate is about 97.00%.In addition,by analyzing the impact of varying input specifications on classification performance,we determine the optimal network data truncation strategy and confirm the model’s excellent generalization ability on different datasets.

Segment routing for traffic engineering and effective recovery in low-earth orbit satellite constellations

Low-Earth Orbit Satellite Constellations(LEO-SCs)provide global,high-speed,and low latency Internet access services,which bridges the digital divide in the remote areas.As inter-satellite links are not supported in initial deployment(i.e.the Starlink),the communication between satellites is based on ground stations with radio frequency signals.Due to the rapid movement of satellites,this hybrid topology of LEO-SCs and ground stations is time-varying,which imposes a major challenge to uninterrupted service provisioning and network management.In this paper,we focus on solving two notable problems in such a ground station-assisted LEO-SC topology,i.e.,traffic engineering and fast reroute,to guarantee that the packets are forwarded in a balanced and uninterrupted manner.Specifically,we employ segment routing to support the arbitrary path routing in LEO-SCs.To solve the traffic engineering problem,we proposed two source routings with traffic splitting algorithms,Delay-Bounded Traffic Splitting(DBTS)and DBTS+,where DBTS equally splits a flow and DBTS+favors shorter paths.Simu-lation results show that DBTS+can achieve about 30%lower maximum satellite load at the cost of about 10%more delay.To guarantee the fast recovery of failures,two fast reroute mechanisms,Loop-Free Alternate(LFA)and LFA+,are studied,where LFA pre-computes an alternate next-hop as a backup while LFA+finds a 2-segment backup path.We show that LFA+can increase the percentage of protection coverage by about 15%.

Network Traffic Synthesis and Simulation Framework for Cybersecurity Exercise Systems

In the rapidly evolving field of cybersecurity,the challenge of providing realistic exercise scenarios that accurately mimic real-world threats has become increasingly critical.Traditional methods often fall short in capturing the dynamic and complex nature of modern cyber threats.To address this gap,we propose a comprehensive framework designed to create authentic network environments tailored for cybersecurity exercise systems.Our framework leverages advanced simulation techniques to generate scenarios that mirror actual network conditions faced by professionals in the field.The cornerstone of our approach is the use of a conditional tabular generative adversarial network(CTGAN),a sophisticated tool that synthesizes realistic synthetic network traffic by learning fromreal data patterns.This technology allows us to handle technical components and sensitive information with high fidelity,ensuring that the synthetic data maintains statistical characteristics similar to those observed in real network environments.By meticulously analyzing the data collected from various network layers and translating these into structured tabular formats,our framework can generate network traffic that closely resembles that found in actual scenarios.An integral part of our process involves deploying this synthetic data within a simulated network environment,structured on software-defined networking(SDN)principles,to test and refine the traffic patterns.This simulation not only facilitates a direct comparison between the synthetic and real traffic but also enables us to identify discrepancies and refine the accuracy of our simulations.Our initial findings indicate an error rate of approximately 29.28%between the synthetic and real traffic data,highlighting areas for further improvement and adjustment.By providing a diverse array of network scenarios through our framework,we aim to enhance the exercise systems used by cybersecurity professionals.This not only improves their ability to respond to actual cyber threats but also ensures that the exercise is cost-effective and efficient.