A Genetic Algorithm for Routing in Packet-Switched Communication Networks

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THE EFFECTIVENESS OF GENETIC ALGORITHM IN CAPTURING CONDITIONAL NONLINEAR OPTIMAL PERTURBATION WITH PARAMETERIZATION “ON-OFF” SWITCHES INCLUDED BY A MODEL

In the typhoon adaptive observation based on conditional nonlinear optimal perturbation (CNOP), the ‘on-off’ switch caused by moist physical parameterization in prediction models prevents the conventional adjoint method from providing correct gradient during the optimization process. To address this problem, the capture of CNOP, when the "on-off" switches are included in models, is treated as non-smooth optimization in this study, and the genetic algorithm (GA) is introduced. After detailed algorithm procedures are formulated using an idealized model with parameterization "on-off" switches in the forcing term, the impacts of "on-off" switches on the capture of CNOP are analyzed, and three numerical experiments are conducted to check the effectiveness of GA in capturing CNOP and to analyze the impacts of different initial populations on the optimization result. The result shows that GA is competent for the capture of CNOP in the context of the idealized model with parameterization ‘on-off’ switches in this study. Finally, the advantages and disadvantages of GA in capturing CNOP are analyzed in detail.

Repulsive firefly algorithm-based optimal switching device placement in power distribution systems

To achieve optimal configuration of switching devices in a power distribution system,this paper proposes a repulsive firefly algorithm-based optimal switching device placement method.In this method,the influence of territorial repulsion during firefly courtship is considered.The algorithm is practically applied to optimize the position and quantity of switching devices,while avoiding its convergence to the local optimal solution.The experimental simulation results have showed that the proposed repulsive firefly algorithm is feasible and effective,with satisfying global search capability and convergence speed,holding potential applications in setting value calculation of relay protection and distribution network automation control.

Performance Evaluation and Analysis of Switching Algorithms in MIMO-OFDM System with Ideal and Non-Ideal CSI

In this paper we analyzed the bit error rate performance of a switching algorithm between spatial multiplex-ing and diversity for an OFDM MIMO system with ideal channel state information. The effect of channel estimation error was studied and we verified by simulations that the spatial multiplexing outperforms the switching algorithm. Given that the switching algorithm is based on the comparison of the channel matrix Demmel condition number to a threshold, its accuracy is compromised when channel estimation error in-creases. As a first intuitive solution, we proceeded to the adaptation of the threshold, but this didn’t lead to a pertinent improvement for the main reason that channel estimation errors did affect the MIMO techniques which use different constellation. Based on that, we proposed a new estimation technique that improved the bit error rate performance significantly.

THE EFFECT OF VP SWITCHES ON THE PERFORMANCE OF BACKUP-VP ALGORITHM IN ATM SELF-HEALING NETWORKS

In this approach, three typical implementation schemes of self-healing function in VP switches are discussed and three corresponding queue models for backup-VP self-healing algorithm are established. Computer simulations are made to these queue models, and detailed analyses are presented to the effect of both the three implementation schemes of self-healing function and some architecture parameters in VP switches on self-healing performance.

Genetic algorithm for multi-protocol label switching

A new method for multi-protocol label switching is presented in this study, whose core idea is to construct model for simulating process of accommodating network online loads and then adopt genetic algorithm to optimize the model. Due to the heuristic property of evolutional method, the new method is efficient and effective, which is verified by the experiments.

多策略融合的蛇优化算法及其应用

针对蛇算法寻优阶段交互性差,初始种群随机程度严重,易陷入局部最优解等问题,提出了一种多策略融合的蛇优化算法(multi-strategy snake optimizer, MSSO)。首先,利用正交矩阵对蛇种群进行初始化,使个体分布更加均匀;其次,设计探索开发阶段切换的自适应方程,用以替换原有的食物量与温度阈值,使算法进行自适应阶段切换;最后,使用联合反向选择策略替换算法原有的新个体孵化方法,提高算法收敛精度的同时加快算法收敛效率。选取10个基准测试函数从不同角度对MSSO算法进行实验,测试算法性能,分析各策略的有效性,并使用Wilcoxon秩和检验来证明算法显著性,通过两个工程应用仿真实验来验证MSSO的实用性。各实验结果表明MSSO较比较算法综合表现更优,证明MSSO算法改进在寻优能力、鲁棒性、实用性等方面均有所提升。

MS-DFM模型的参数估计及其在股市周期识别中的应用

对已有的马尔科夫转移动态因子模型提出了一个新的参数估计方法——两步最大期望(Expectation-maximization,EM)法,通过对马尔科夫转移动态因子模型进行重新参数化,将其转换为混合动态因子模型,并将因子与状态均视为潜变量,利用EM算法实现对重新参数化后的参数以及因子得分的估计。将因子得分视为已知数据、状态视为潜变量,针对每个因子序列建立马尔科夫转移自回归模型,利用EM算法对依状态变化的截距项和自回归系数进行估计,并对状态与拐点进行识别。通过数值模拟验证该方法的有效性,并将该模型与估计方法用于我国沪深股市股票数据分析中,对股市行业周期进行度量和识别。

改进蛇算法的七电平逆变器SHEPWM研究

多电平逆变器控制策略中的特定谐波消除脉宽调制(SHEPWM)能够以较低的开关频率得到较好的电压输出波形,重点在于开关角的求取。在多电平逆变器SHEPWM调制的开关角求解算法上提出改进蛇优化算法,对传统蛇优化算法采用Tend混沌映射初始种群,使初始种群分布更加均匀;引入Levy飞行策略增强算法的全局搜素能力,能够更加精准快速得到全局开关角的最优解。仿真结果表明,改进蛇优化算法所求的开关角相较于传统算法更好地消除低次谐波,并降低了输出线电压的畸变率,充分验证了该方法的可行性。

Event-Triggered Zero-Gradient-Sum Distributed Algorithm for Convex Optimization with Time-Varying Communication Delays and Switching Directed Topologies

Nowadays, distributed optimization algorithms are widely used in various complex networks. In order to expand the theory of distributed optimization algorithms in the direction of directed graph, the distributed convex optimization problem with time-varying delays and switching topologies in the case of directed graph topology is studied. The event-triggered communication mechanism is adopted, that is, the communication between agents is determined by the trigger conditions, and the information exchange is carried out only when the conditions are met. Compared with continuous communication, this greatly saves network resources and reduces communication cost. Using Lyapunov-Krasovskii function method and inequality analysis, a new sufficient condition is proposed to ensure that the agent state finally reaches the optimal state. The upper bound of the maximum allowable delay is given. In addition, Zeno behavior will be proved not to exist during the operation of the algorithm. Finally, a simulation example is given to illustrate the correctness of the results in this paper.

基于开关电感电池均衡器的CCM软开关实现算法

针对开关电感的锂离子电池均衡器在小功率便携式用电设备中仍有很强的实用价值,但传统控制策略控制的均衡器中开关管为硬开关的问题,提出1种基于开关电感锂电池均衡器电流连续运行模式下的软开关实现算法。分析均衡器电感电流电池单体充、放电电流大小的决定因素,由此提出均衡器中开关管的软开关实现算法并分析了该算法的控制精度。通过检测电池单体的电压与充电电流的变化率可实时得到电池单体的等效阻抗与开路电压,进一步保证了算法的精度。实验结果表明,所提软开关控制算法性能良好。

考虑样本多样性的转辙机故障诊断决策模型

为解决现有研究未充分考虑诊断模型在不同转辙机数据样本条件下的适用性,且单一诊断方法难以满足故障诊断精度与决策要求的问题,提出一种考虑样本多样性的故障诊断决策模型.首先,从ZYJ7电液转辙机8种故障模式和正常模式所对应的油压曲线中分别提取时域、频域、时频域特征量,采用基于核函数的主成分分析法对3个域内的特征量分别降维,得到每个域的特征矩阵,进而构成不同类型的数据样本.然后,基于PSO-KNN、SA-PSO-PNN、PSO-SVM算法构建决策模型.当样本是一般数据样本时,决策模型采用3种算法分别做同一域数据分类,并对同一域各算法诊断结果进行三取二表决,分别得到每一域诊断结果;当样本是大数据样本、不均衡数据样本时,决策模型根据不同样本特点采用3种算法中的推荐算法得到每一域诊断结果.最后,利用决策模型对各域诊断结果进行三取二表决得到最终诊断结果 .仿真结果表明:与单一诊断算法相比,决策模型在大数据样本下,平均准确率提升1.01%;在不均衡数据样本条件下,决策模型的平均准确率提升12.82%;在一般数据样本下,决策模型平均准确率提升6.18%.决策模型通过结合多域的多维特征与各算法特点提高了诊断精度,为集成学习在转辙机故障诊断领域应用提供了一种思路.

基于迭代轮询的光交换机时延确保调度算法(特邀)

【目的】数据中心庞大的数据传输及交换业务量对交换设备有着很高的性能要求,不仅需要具备良好的扩展性,还需提供大带宽和低切换时延。快速光交换机具有切换速率极快和接入带宽远大于电交换机的优越性能,面对现今愈发庞大的数据交换需求,为实现更高的接入速率和更好的处理性能,交换机结构将向全光方向发展。其中,针对快速光交换机的调度算法研究多以提供100%吞吐率为目标,在提供服务质量(QoS)保证方面的研究相对较少。【方法】对此,文章基于矩阵分解和业务整形的思想,采用迭代与轮询相结合的方法,提出了一种时延确保的光交换机迭代调度算法。文章提出的迭代轮询(IRR)调度算法首先将业务速率矩阵映射为业务矩阵,然后基于业务矩阵的服务要求计算得到服务要求矩阵序列,最后从服务要求矩阵序列中提取调度矩阵序列。【结果】理论分析证明,IRR调度算法能够提供基于端口的时延确保,并且给出了在给定业务条件下时延上界的计算方法。【结论】IRR调度算法可应用于光交换机的时延性能优化与时延确保光交换系统的设计中,为下一代全光网络具备确定性QoS提供关键支撑。

基于可视图特征与CatBoost的转辙机故障诊断

针对道岔转辙机转换油压信号难以提取有效的故障特征且传统的故障诊断方式效果差的问题,本文提出一种基于可视图特征与CatBoost的道岔转辙机故障诊断方法。首先,采用可视图算法将时域信号转换为复杂网络图。然后,提取复杂网络图的5种统计特征,即网络平均度、全局聚类系数、平均路径长度、传递性特征与网络图密度。最后,通过CatBoost算法实现道岔转辙机故障诊断。将该方法分别与其它特征提取方法、故障分类算法进行比较。实验结果表明:可视图特征能够更有效表征道岔转辙机的工作状态,CatBoost算法对道岔转辙机4种工作状态的诊断准确率达到97.5%,验证了该方法的有效性和优越性。

蝴蝶优化算法对大青杨生长速率预测模型的改进

为提高大青杨生长速率的预测精度,提出了一种基于改进的蝴蝶优化算法(improved butterfly optimization algorithm, IBOA)与径向基函数(radial basis function, RBF)神经网络结合的预测木材材性方法。通过使用佳点集法对标准蝴蝶算法中的种群进行初始化,将自适应切换频率和Levy飞行相结合进一步优化人工蝴蝶算法。构建出了新的IBOA-RBF神经网络木材材性预测模型,将得到的结果与其他几种算法优化的RBF神经网络预测结果进行对比。结果表明:基于IBOA-RBF神经网络模型预测效果最好,收敛速度从37步降低到了23步,预测结果误差达到了5.72%,预测精度最高。可见,对蝴蝶算法的改进是可行的,且对相关人员定向培养大青杨起到较大的帮助。

动态环境下基于换电模式的电动车路径问题研究

在实际配送中,考虑车速受不同天气、不同时段的影响,根据电动车辆行驶速度与能耗之间的函数关系,以电动车固定行驶成本、能耗成本、时间惩罚成本构成的总运输费用最小为目标,以车辆载重、车辆电池容量、客户时间窗、车辆速度为约束条件,构建动态环境下基于换电模式和时变速度的电动汽车路径优化模型。用遗传算法对模型求解,采用实数编码、轮盘赌选择算子、随机单点交叉、随机变异的方式,并以小变异率提高局部搜索能力,然后与车辆速度恒定时的几种情况进行对比。试验结果表明,所建立的优化模型更符合实际情况,能够根据客户属性和动态环境下的路网特性,合理安排发车、规划配送路径和顺序,从而降低配送成本,为物流企业运营车辆和优化配送提供参考。

基于图搜索的陆空两栖平台3D路径规划算法

为了解决陆空两栖平台路径规划能效与寻径效率问题,提出了一种基于图搜索算法的全局路径规划算法。考虑了空中飞行能耗与地面行驶能耗模型的不同,结合起飞阈值、地面移动成本系数、空中移动成本系数等参数决定模态切换机制,在遇到难以越过的障碍时使用逃脱算法脱困,当允许地面通过时优先采用地面行驶的方式到达目标点以提升寻径效率与能效。为验证该算法,分别建立了针对陆空2种模式下的能耗模型,在现有交通规则约束下完成城市场景和虚拟迷宫测试。结果表明:本算法的搜索效率相较于对照组算法提升了30%以上,有效提升搜寻效率的同时降低了能量消耗。

基于CIOQ架构改进iSLIP调度算法的研究

为了提高数据在传输过程中的性能指标和用户服务质量,针对CIOQ架构和iSLIP调度算法进行研究,提出了一种优先匹配队列信元数量达到满载的PMQF_iSLIP调度算法。算法先判断VOQ队列中的信元数量是否等于设置的最大容量,再根据判断结果执行不同的算法步骤,最终更新已完成匹配端口的优先级,在下一次匹配时,使端口具有优先匹配的权力。最后通过自己开发的数据交换仿真平台,对改进的算法进行仿真和分析,仿真结果表明,在相同的流量模型下,改进的算法减少了丢包率,增加了吞吐率。

基于人工智能的计算机网络交换机决策算法

传统的交换机决策算法主要基于固定的规则和策略,难以适应复杂多变的网络环境。随着人工智能技术的快速发展,机器学习和深度学习算法成为改进交换机决策的有力工具。这些算法能够通过学习和训练来自动调整交换机的决策策略,提高网络的效率和可靠性。主要介绍基于人工智能的计算机网络交换机决策算法的理论基础、应用技术和算法设计,以及几种具体的算法示例。