A Multi-Constraint Path Optimization Scheme Based on Information Fusion in Software Defined Network

The existingmultipath routing in Software Defined Network (SDN) is relatively blind and inefficient, and there is alack of cooperation between the terminal and network sides, making it difficult to achieve dynamic adaptationof service requirements and network resources. To address these issues, we propose a multi-constraint pathoptimization scheme based on information fusion in SDN. The proposed scheme collects network topology andnetwork state information on the network side and computes disjoint paths between end hosts. It uses the FuzzyAnalytic Hierarchy Process (FAHP) to calculate the weight coefficients of multiple constrained parameters andconstructs a composite quality evaluation function for the paths to determine the priority of the disjoint paths. TheSDN controller extracts the service attributes by analyzing the packet header and selects the optimal path for flowrule forwarding. Furthermore, the service attributes are fed back to the path composite quality evaluation function,and the path priority is dynamically adjusted to achieve dynamic adaptation between service requirements andnetwork status. By continuously monitoring and analyzing the service attributes, the scheme can ensure optimalrouting decisions in response to varying network conditions and evolving service demands. The experimentalresults demonstrated that the proposed scheme can effectively improve average throughput and link utilizationwhile meeting the Quality of Service (QoS) requirements of various applications.

A LEVEL SET METHOD FOR STRUCTURAL TOPOLOGY OPTIMIZATION WITH MULTI-CONSTRAINTS AND MULTI-MATERIALS

Combining the vector level set model,the shape sensitivity analysis theory with the gradient projection technique,a level set method for topology optimization with multi-constraints and multi-materials is presented in this paper.The method implicitly describes structural material in- terfaces by the vector level set and achieves the optimal shape and topology through the continuous evolution of the material interfaces in the structure.In order to increase computational efficiency for a fast convergence,an appropriate nonlinear speed mapping is established in the tangential space of the active constraints.Meanwhile,in order to overcome the numerical instability of general topology opti- mization problems,the regularization with the mean curvature flow is utilized to maintain the interface smoothness during the optimization process.The numerical examples demonstrate that the approach possesses a good flexibility in handling topological changes and gives an interface representation in a high fidelity,compared with other methods based on explicit boundary variations in the literature.

The Information Modeling and Intelligent Optimization Method for Logistics Vehicle Routing and Scheduling with Multi-objective and Multi-constraint

The vehicle routing and scheduling (VRS) problem with multi-objective and multi-constraint is analyzed, considering the complexity of the modern logistics in city economy and daily life based on the system engineering. The objective and constraint includes loading, the dispatch and arrival time, transportation conditions,total cost,etc. An information model and a mathematical model are built,and a method based on knowledge and biologic immunity is put forward for optimizing and evaluating the programs dimensions in vehicle routing and scheduling with multi-objective and multi-constraints. The proposed model and method are illustrated in a case study concerning a transport network, and the result shows that more optimization solutions can be easily obtained and the method is efficient and feasible. Comparing with the standard GA and the standard GA without time constraint,the computational time of the algorithm is less in this paper. And the probability of gaining optimal solution is bigger and the result is better under the condition of multi-constraint.

Surface Deformation with Multi-constraints Based on the Minimum Energy

A method for deformation of 3D point clouds models was proposed with multi-constraints including arc-length constraints and multi-points position constraints. The energy function was built for the polyline which had been converted from the curve. Based on the minimum energy curve method, the curve on the mesh was deformed. The test results show that the proposed method has good performance. Compared with the other method,shape preserving of the curve is better. Finally,this method is used for the deformation of the 3D mannequin model. Circumference changes of the mannequin model can be reflected by the arc-length change in the size of the cross section.

3D Path Planning of the Solar Powered UAV in the Urban-Mountainous Environment with Multi-Objective and Multi-Constraint Based on the Enhanced Sparrow Search Algorithm Incorporating the Levy Flight Strategy

In response to practical application challenges in utilizing solar-powered unmanned aerial vehicle(UAV)for remote sensing,this study presents a three-dimensional path planning method tailored for urban-mountainous environment.Taking into account constraints related to the solar-powered UAV,terrain,and mission objectives,a multi-objective trajectory optimization model is transferred into a single-objective optimization problem with weight factors and multiconstraint and is developed with a focus on three key indicators:minimizing trajectory length,maximizing energy flow efficiency,and minimizing regional risk levels.Additionally,an enhanced sparrow search algorithm incorporating the Levy flight strategy(SSA-Levy)is introduced to address trajectory planning challenges in such complex environments.Through simulation,the proposed algorithm is compared with particle swarm optimization(PSO)and the regular sparrow search algorithm(SSA)across 17 standard test functions and a simplified simulation of urban-mountainous environments.The results of the simulation demonstrate the superior effectiveness of the designed improved SSA based on the Levy flight strategy for solving the established single-objective trajectory optimization model.

A multi-constraints based pose coordination model for large volume components assembly

In the assembly process of large volume product,engineering constraints limit the relative pose of components and serve as a standard for judging assembly quality.However,in the traditional process of target pose estimation,a general method is needed for establishing the correlation between engineering constraints and product pose,and it is difficult to evaluate pose by constraints comprehensively.Therefore,the process of target pose estimation and evaluation is separated.In this paper,a pose coordination model based on multi-constraints is proposed,which includes pre-processing,pose estimation,pose adjustment and evaluation.Firstly,engineering constraints are decoupled into 4 types of Minimum Geometrical Reference Constraints(MGRC),and the inequalities for solving target pose are formulated.Then the Constraint Coordination Index(CCI)is defined as the optimization objective to solve the target pose.Finally,with CCI as the numerical index,the target pose is evaluated to illustrate the quality of assembly.Taking the simulation experiment of wing-fuselage jointing as an example,the external and internal parameters of model are analyzed,and the pose estimation based on multi-constraints reduces the CCI by 12%,compared with the point-set-registration method.

Output tracking of delayed logical control networks with multi-constraint

In this study,the output tracking of delayed logical control networks(DLCNs)with state and control constraints is further investigated.Compared with other delays,state-dependent delay updates its value depending on the current state values and a pseudo-logical function.Multiple constraints mean that state values are constrained in a nonempty set and the design of the controller is conditioned.Using the semi-tensor product of matrices,dynamical equations of DLCNs are converted into an algebraic description,and an equivalent augmented system is constructed.Based on the augmented system,the output tracking problem is transformed into a set stabilization problem.A deformation of the state transition matrix is computed,and a necessary and sufficient condition is derived for the output tracking of a DLCN with multi-constraint.This condition is easily verified by mathematical software.In addition,the admissible state-feedback controller is designed to enable the outputs of the DLCN to track the reference signal.Finally,theoretical results are illustrated by an example.

组合缓冲约束下的多目标混合流水线节能调度

为解决生产阶段间带有无限缓冲和阻塞两种中间缓冲约束的混合流水线节能调度问题,考虑不相关并行机和多时间约束建立数学模型,结合问题特征提出一种改进多目标模因算法以同时最小化最大完工时间和机器总能耗。采用基于不相关机器分配的矩阵编码方案,利用基于Tent混沌映射的混合初始化策略生成初始元胞数组,全局优化算子应用基于参数的自适应遗传策略改进的非支配排序遗传算法,局部增强搜索算子应用一种融合自适应选择邻域搜索和多目标模拟退火的搜索策略以提高算法搜索能力。通过24种不同规模问题的算例实验,验证了所提算法求解该问题的有效性和优越性。实验结果表明:改进多目标模因算法在平均运行时间241.26 s内得到的平均IGD值为47.89,平均SP值为857.25,均低于其他3种对比算法。改进多目标模因算法所求解集具有较好的收敛性、多样性和分布性。

考虑FACTS设备和可再生能源的多目标优化调度

随着高比例可再生能源接入电网,增加了电网调度和运行的难度。针对这一问题,本文提出一种基于自适应罚函数的多目标竞争粒子群算法,用于解决包含随机性可再生能源和多种柔性交流输电系统设备的多目标优化问题。首先,构建完整的交流潮流模型与多样化连续/离散控制变量约束,以降低燃料成本、电压偏差、输电损耗和排放量为优化目标;然后,探讨不同柔性交流输电系统设备配置下这些目标的平衡;最后,通过修改的IEEE 30节点系统分析结果得出,综合考虑可再生能源和柔性交流输电系统设备的优化配置,可以显著降低排放和燃料成本,提高电网的可靠性和输电效率。与现有方法相比,所提出的算法在多目标优化问题上表现出更高的解质量和稳定性。

考虑能量转换装置可变效率的综合能源系统多目标优化调度

为了全面考虑综合能源系统的运行性能,提出一种计及能量转换装置可变效率的综合能源系统多目标优化运行方法。首先,对具有可变效率的能量转换设备运行特性曲线进行分段线性化处理。其次,建立以经济成本、碳排放量和综合能源利用效率为优化对象的多目标优化调度模型,并通过Charnes-Cooper变换解决综合能源利用效率目标的非凸非线性问题。最后,在求解该多目标模型时,运用规格化法平面约束法生成均匀分布的帕累托前沿,再采用改进熵权双基点法决策出折衷最优解。对修改的IEEE 33节点电网和8节点热网耦合的综合能源系统进行仿真分析。结果表明:优化后的模型和算法可以得到兼顾综合能源系统经济性、低碳性及高效性的优化运行方案,为决策者提供科学的决策参考。

图神经网络引导的演化算法求解约束多目标优化问题

约束多目标优化问题由于其约束复杂性、可行域不规则性和可行解稀疏性,通常存在难以精准刻画约束关系,以及难以找到收敛性好且分布均匀的帕累托非支配解等问题。为此,本文提出了一种图神经网络引导的约束多目标演化算法,该算法包括了学习模块与权向量自适应策略,其中学习模块通过训练图神经网络对解集进行快速评估,权向量自适应策略通过判别准则和更新机制增强种群多样性。实验结果表明,该算法在多个基准测试问题上显著优于现有的五个先进算法,在复杂约束多目标优化问题上表现出色。

计及多目标约束的成品卷烟销售订单配送调度优化

针对制造业供应链中的跨地域多运输中心大数据订单物流调度问题,对涉及多约束条件和多目标的异构运输车辆调度问题展开研究。根据烟草制造工业的实际运输任务需求,考虑制造订单安排、车辆准运证限制、运输车辆物理限制、运输时间窗、排产出库时间等实际约束,搭建了以包括车辆等待卷烟出库的最短运输时间,最低运输费用及最高车辆利用率为优化目标的成品烟物流车辆跨地域调度优化模型。设计了基于差分进化的混合多元宇宙算法(DE-MVO)用以解决该模型。最后基于某烟草工业企业的实际生产订单数据进行实验,将所设计的算法分别与粒子群(PSO)算法、差分进化(DE)算法、鲸鱼优化(WOA)算法、遗传优化(GA)算法、乌鸦搜索(CSA)算法、天鹰座优化器(AO)、多元宇宙(MVO)等前沿算法进行横向对比,对比结果表明所设计的算法在全局搜索能力、寻优精度等方面更优于其他算法,进一步表明所建立的模型在求解制造业供应链运输车辆调度问题中具有可行性。

基于平均一致协议的分布式自适应多智能体聚集控制

分布式聚集控制问题是多智能体协同控制中的一个重要问题。由于智能体的可移动性和感知能力有限,传统的分布式聚集算法难以保证连通性,从而聚集成多个簇群。此外,去中心化的大规模聚集控制给获取全局聚集点带来了巨大的挑战。针对连通性保护问题,基于平均一致协议与约束集,提出了一个带有连通性约束的多智能体聚集协议(Multi-Agent Rendezvous Protocol with Connectivity constraints,MARP-CC)。然后针对聚集点无法预测的问题,提出了位置合成(Location Synthesis Strategy,LSS)和位置重定向(Location Redirection Strategy,LRS)两种控制策略。智能体根据当前连通情况,自适应选择最优的控制策略进行迭代。结合这两种控制策略,提出了带连通性约束的分布式自适应多智能体聚集算法(Distributed Adaptive Multi-Agent Rendezvous algorithm with Connectivity Constraints,DAMAR-CC)。对算法的收敛性和连通性进行了分析,并通过大量的仿真说明了DAMAR-CC可以引导智能体稳定地聚集在初始拓扑的几何中心。

基于分层约束强化学习的综合能源多微网系统优化调度

构建多微网系统是消纳可再生能源、提升电网稳定性的有效方式。通过各微网的协调调度,可有效提升微网的运行效益以及可再生能源的消纳水平。现有多微网优化问题场景多元,变量众多,再加上源荷不确定性及多微网主体的数据隐私保护等问题,为模型的高效求解带来了巨大挑战。为此,该文提出了一种分层约束强化学习优化方法。首先,构建了多微网分层强化学习优化框架,上层由智能体给出各微网储能优化策略和微网间功率交互策略;下层各微网以上层策略为约束,基于自身状态信息采用数学规划法对各微网内部的分布式电源出力进行自治优化。通过分层架构,减小通信压力,保护微网内部数据隐私,充分发挥强化学习对源荷不确定性的自适应能力,大幅提升了模型求解速度,并有效兼顾了数学规划法的求解精度。此外,将拉格朗日乘子法与传统强化学习方法相结合,提出一种约束强化学习求解方法,有效地解决了传统强化学习方法难以处理的约束越限问题。最后通过算例验证了该方法的有效性和优势。

融合设备状态和产品要求的电解铜箔制造工艺参数优化

针对当前电解铜箔制造工艺参数的优化仅考虑产品要求的问题,提出了一种融合设备状态和产品要求的电解铜箔制造工艺参数优化方法。首先,为辨识铜箔制造中工艺、设备与产品参数之间的非线性关系,建立了基于神经网络的“工艺-设备”与产品参数的映射模型,并通过构建当前生产模式与历史生产模式的相似性度量方法,确定当前工艺参数的优化初始点;然后,基于所建立的映射模型、设备状态和产品要求构建多目标约束函数,并利用遗传算法优化工艺参数,获得同时满足设备状态和产品要求的最佳工艺参数。企业的生产验证表明:相较于仅考虑产品要求的工艺优化方法,所提方法优化后的工艺参数能在相同的设备状态下获得质量更好的产品,在常温/高温抗拉强度、常温/高温延伸率和树脂剥离强度这5项关键指标上,分别提升了6.1 MPa、6.1 MPa、1.9%、0.7%和0.22 N/mm,生箔制备和表面处理的生产效率也提高了2.65%和7.5%,能够有效保证并提高铜箔的质量和生产效益。

基于GA-SA组合算法的山区复杂环境无人机起降点选址

针对山区复杂环境下的物流链前端无人机货运起降点选址和任务分配进行研究。首先以建设成本最小和运输时间满意度最大为目标,综合考虑无人机自身性能和禁飞空域等因素,构建多约束条件下多目标函数的起降点选址和任务分配模型。采用遗传算法(genetic algorithm, GA)和模拟退火算法(simulated annealing algorithm, SA)的组合算法进行求解,首先通过遗传算法得出较优的可行解,再以此解作为退火算法的初始解进行模型求解。仿真结果表明,构建的多约束模型能够实现预期效果,并且采用的算法解决此类问题时具有良好的适用性。

基于多策略改进鲸鱼算法的船舶推力分配方法

船舶动力定位推力分配求解是一种复杂的多约束多目标优化问题,鲸鱼优化算法(WOA)处理该类问题较传统推力分配算法计算更简便、精度更高,但以牺牲时间为代价,需解决实时可靠收敛问题。针对上述问题,提出一种改进鲸鱼推力分配算法,通过采用等式约束项的特解系数为个体变量,降低搜索空间维度和范围,为算法获取可靠解和快速收敛奠定基础;通过空间区块化和“精英”筛选法初始化种群,增加初始种群分散性和“精英”个体数量,提高寻优效率和收敛稳定性;引入动态种群边界获取合理的搜索空间,提高算法收敛速度;最后运用类梯度法进行种群更新,模拟区域遍历搜寻,以较少迭代次数获取可靠解。以CybershipⅢ船模为对象进行仿真验证,结果表明该算法可行,具有较好的实时性和稳定收敛性,提高了推力分配性能。

地理知识图谱增强与多时空条件约束的轨迹预测

基于机器学习的轨迹预测方法通常依赖历史轨迹数据的数量和质量,而社交媒体签到数据更新频率低,形成的轨迹稀疏,在预测中易出现难学习、过拟合等问题。为突破低质量轨迹数据在预测任务中的限制,本文提出一种基于地理知识图谱增强与多时空约束条件建模的轨迹预测方法。将复杂异构的多源地理信息结构化为由若干三元组构成的地理知识图谱进行统一表达,并通过知识表示模型挖掘其中语义关联来增强轨迹序列的向量表征,同时采用具有多重时空约束条件的多头自注意力机制提取稀疏轨迹序列中的多重时空特征,从而提升轨迹预测精度。研究采用纽约市Foursquare社交媒体签到数据进行方法验证,试验结果表明:本文方法相较于其他表示学习方法和轨迹预测方法,在命中率和平均倒数排名两个评价指标上均有不同程度的提升,能够有效增强稀疏轨迹序列的表征,提取轨迹的多重时空特征,提高社交媒体用户签到轨迹的预测精度。

空间环境对引信的挑战与对策

随着人类对临近空间和外太空资源的开发与利用不断深入,航天大国围绕空间资源和主权的对抗逐步升级,随之空间对抗武器装备及其相关技术成为发展的重点。空间存在着各种极端且复杂的环境及其效应,其对空间武器装备的影响机理尚不明晰,为此,在分析空间环境效应对空间对抗弹药影响的基础上,从环境识别、安全性、可靠性、目标毁伤四方面提出了空间对抗弹药引信的设计约束,研判了未来发展中面临的空间环境特征识别与利用、空间环境效应防护、武器系统高效信息交联、高精度起爆控制以及空间环境效应地面模拟等技术挑战。针对空间环境下引信技术发展的挑战论证了需优先发展的关键技术与应对策略。

基于CutMix数据增强与多约束损失函数的YOLOv7盾构隧道渗漏水检测

盾构隧道强度投影图像中渗漏水尺寸不一致且像素占比偏小,现有目标检测模型的关键特征学习能力较弱,存在渗漏水病害目标检测精度偏低的问题。本文提出了基于CutMix数据增强与多约束损失函数的改进YOLOv7盾构隧道渗漏水检测方法。首先采用镶嵌CutMix方法对隧道图像进行数据增强,将多张不同的训练样本进行随机裁剪,拼接融合成具有综合特征的新样本;然后以YOLOv7网络为骨架结构,引入高效通道注意力模块,提高渗漏水关键特征的自主学习与表达能力;最后引入多约束几何条件的损失函数,提高预测框几何形状的精度,从而提升模型预测精度。在光线良好、光线不佳和存在遮挡等复杂环境情况下,选取Fast R-CNN、SSD、YOLOv5、YOLOv7这4种算法进行对比,试验表明,本文算法渗漏水检测精度达85.90%,平均精度比同类算法分别提高5.55%、8.89%、3.93%、2.75%,具有较高的稳健性和泛化能力。