A novel design of semi-active hydraulic mount with wide-band tunable notch frequency

Hydraulic engine mount is advanced vibration isolator with superior performance to reduce vibration transferred from engine to chassis. As the stiffness at notch frequency is small, some semi-active or active hydraulic mounts tune some parameters to let notch frequency coincide with exciting frequency for better vibration isolation performance. It is discovered the current semi-active mounts can tune the notch frequency in narrow frequency band when only one parameter is tuned. A novel semi-active hydraulic engine mount design which introduces screw thread is proposed and researched in the paper. This hydraulic mount can control both cross section area and the length of inertia track and the theoretical tunable notch frequency band is [0, ∞). Theoretical work is carried out to uncover the capability for the proposed design to tune notch frequency. Simulation work is performed to understand its high vibration isolation performance. For the purpose of energy conservation, the friction self-locking is introduced. This denotes once the mount is tuned at optimal condition, the energy can be cut off and the optimal condition will never change. We also determine the best time to tune the parameters of the proposed mount in order to decrease the acting force. The proposed semi-active mount has capability to obtain wide band tunable notch frequency and has merit of energy conservation.     

Double-notch single-pumper fluid mounts

Passive fluid mounts are widely used in the automotive and aerospace applications to isolate the cabin from the engine noise and vibration. In the case of aerospace fixed wing applications, when fluid mounts are used, they are placed in between the engine and the fuselage, and the notch frequency of each fluid mount is tuned to either N1 frequency (engine low speed shaft imbalance excitation frequency) or to N2 frequency (engine high speed shaft imbalance excitation frequency) at the cruise condition. Since current passive fluid mount designs have only one notch, isolation is only possible at N1 or at N2, but not both. Here, in this paper, a double-notch passive fluid mount design will be presented, which has two notch frequencies, and therefore can provide vibration and noise isolation at two frequencies. In this paper, the new fluid mount design concept and its mathematical model and simulation results will be presented.     

一种基于非基因信息的免疫记忆优化算法

为提高人工免疫优化算法的优化能力, 将非基因信息的记忆机制引入智能算法, 提出了一种基于非基因信息的免疫记忆优化算法. 算法通过对先验知识(非基因信息)的短期记忆并指导后续进化, 降低盲目搜索和重复搜索, 增加了搜索的智能性和有效性. 结合标准测试函数在高维下的仿真实验表明, 与其他智能算法相比, 新算法在收敛速度、收敛精度和全局收敛性方面均优于对比算法. 此外, 在超高维下的仿真结果表明新算法具有在大规模维度解空间中的全局寻优能力.     

X波段微带余割平方扩展波束天线阵赋形优化遗传算法研究

提出了一种改进型自适应遗传算法, 该算法用logistic函数拟合交叉概率和变异概率, 以赌轮盘选择和精英保留相结合的方式, 在全局寻找最优解. 与经典遗传算法相比, 改进型自适应遗传算法可以大大提高算法的求解质量. 本文基于改进的自适应遗传算法研究设计了-3 dB范围为0°-12°, -10 dB波束宽度为65°, 波束覆盖为65°, 天线频带范围为8.5-9.8 GHz, 中心频率为9.05 GHz的X波段微带余割平方扩展波束天线阵. 研究结果表明改进型自适应遗传算法对方向图的拟合程度具有较大提高, 适应度值可以从0.07以下提升到0.09以上.     

布里渊散射谱参数提取问题的混合优化算法研究与应用

提出了一种将Particle Swarm Optimization(PSO)算法和Levenberg-Marquardt(LM)算法按概率混合优化的新算法,将其用于Pseudo-Voigt型布里渊散射谱以提高其拟合度和频移提取精度。新算法以PSO算法为主框架,首先用PSO算法进行全局搜索,在优化一定次数后每次优化随机产生一个概率rand(0,1),若rand(0,1)小于或等于事先设定的概率P,则把PSO算法得到的最优解作为LM算法的初值进行局部深度搜索,将LM算法得到的最优解替换先前PSO算法的最优解,继续使用PSO算法进行全局搜索;若rand(0,1)大于P则仍然用PSO算法搜索,等待下一次优化产生随机概率rand(0,1)进行判断,交替使用两种算法直至获得理想的全局最优解。仿真分析和实验表明,新算法克服了单一算法的缺点,提高了布里渊散射谱的拟合度和频移提取的精度,充分证明了新算法的实用性和可行性。     

Optical axis jitter rejection for double overlapped adaptive optics systems

Optical axis jitters, or vibrations, which arise from wind shaking and structural oscillations of optical platforms, etc., cause a deleterious impact on the performance of adaptive optics systems. When conventional integrators are utilized to reject such high frequency and narrow-band disturbance, the benefits are quite small despite their acceptable capabilities to reject atmospheric turbulence. In our case, two suits of complete adaptive optics systems called double overlapped adaptive optics systems (DOAOS) are used to counteract both optical jitters and atmospheric turbulence. A novel algorithm aiming to remove vibrations is proposed by resorting to combine the Smith predictor and notch filer. With the help of loop shaping method, the algorithm will lead to an effective and stable controller, which makes the characteristics of error transfer function close to notch filters. On the basis of the spectral analysis of observed data, the peak frequency and bandwidth of vibrations can be identified in advance. Afterwards, the number of notch filters and their parameters will be determined using coordination descending method. The relationship between controller parameters and filtering features is discussed, and the robustness of the controller against varying parameters of the control object is investigated. Preliminary experiments are carried out to validate the proposed algorithms. The overall control performance of DOAOS is simulated. Results show that time delays are a limit of the performance, but the algorithm can be successfully implemented on our systems, which indicate that it has a great potential to reject jitters.     

Improved Local Search with Momentum for Bayesian Networks Structure Learning

Bayesian Networks structure learning (BNSL) is a troublesome problem that aims to search for an optimal structure. An exact search tends to sacrifice a significant amount of time and memory to promote accuracy, while the local search can tackle complex networks with thousands of variables but commonly gets stuck in a local optimum. In this paper, two novel and practical operators and a derived operator are proposed to perturb structures and maintain the acyclicity. Then, we design a framework, incorporating an influential perturbation factor integrated by three proposed operators, to escape current local optimal and improve the dilemma that outcomes trap in local optimal. The experimental results illustrate that our algorithm can output competitive results compared with the state-of-the-art constraint-based method in most cases. Meanwhile, our algorithm reaches an equivalent or better solution found by the state-of-the-art exact search and hybrid methods.     

A Hybrid Rao-NM Algorithm for Image Template Matching

This paper proposes a hybrid Rao-Nelder–Mead (Rao-NM) algorithm for image template matching is proposed. The developed algorithm incorporates the Rao-1 algorithm and NM algorithm serially. Thus, the powerful global search capability of the Rao-1 algorithm and local search capability of NM algorithm is fully exploited. It can quickly and accurately search for the high-quality optimal solution on the basis of ensuring global convergence. The computing time is highly reduced, while the matching accuracy is significantly improved. Four commonly applied optimization problems and three image datasets are employed to assess the performance of the proposed method. Meanwhile, three commonly used algorithms, including generic Rao-1 algorithm, particle swarm optimization (PSO), genetic algorithm (GA), are considered as benchmarking algorithms. The experiment results demonstrate that the proposed method is effective and efficient in solving image matching problems.     

一种基于多准则决策和PSO-LM混合优化算法的多峰Brillouin散射谱的特征提取方法

采用传统方法对多峰Brillouin散射谱进行拟合的过程中,通常是以谱线最大功率点为基准的,却忽略了其他比该点小但却是极值的功率点。这样获得的拟合曲线通常只有一个峰值,相当于把除最高峰之外还有多个小峰的多峰Brillouin散射谱进行了简化,导致大量有用信息的丢失。为了提高Brillouin散射谱的特征提取精度,提出了一种基于MCDM和PSO-LM混合优化算法的多峰Brillouin散射谱特征提取方法(MCDM-PSO-LM)。MCDM可以识别和准确定位多峰Brillouin散射谱的各个波峰和波谷;PSO-LM混合优化算法可以实现分别对各个波峰和波谷的曲线进行拟合并找到每一个波峰的中心频率,该算法既克服了PSO算法过早收敛于局部极值和LM算法依赖初值的问题,又可以将PSO算法的全局搜索能力和LM算法的局部收敛能力结合在一起。较传统算法而言,MCDM-PSO-LM算法保证了对最优值求解的速度和精度,提高了运算能力,使解析解最大限度地接近最优值。分别在不同信噪比和不同线宽条件下进行仿真验证,频移和温度误差分析结果表明,MCDM-PSO-LM方法可以对多峰Brillouin散射谱的各个波峰与波谷进行准确定位,可用于多峰Brillouin散射谱的特征提取,识别效果明显强于传统算法,提高了信息分析的准确性。     

糖厂澄清工段生产指标建模及操作参数优化

针对亚法糖厂澄清工段清汁色值和清汁残硫量难以在线测量的问题,提出了一种基于人工蜂群优化的在线极限学习机软测量方法。先用核主元分析法确定影响清汁质量的关键参数,建立基于在线极限学习机的软测量模型。同时利用人工蜂群算法对在线极限学习机的隐层参数进行寻优,优化所建模型。最后,使用带约束的粒子群对软测量模型进行优化求解,得到典型工况下的最优操作设定值,为后续工况操作提供参考依据。仿真结果表明,基于人工蜂群优化的在线极限学习机模型能够准确地预测清汁色值和残硫量,同时基于此模型优化的操作参数设定值能够达到期望的指标。     

Synthesis of Binary Computer－Generated Hologram by Optimal Recursive Direct Binary Search Algorithm

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二冷配水优化建模与混合自适应PSO算法求解

针对连铸二冷区生产环境复杂且存在着大量水雾干扰的情况,建立了连铸水量优化模型并提出了一种混合的自适应粒子群算法来求解连铸二冷水优化问题。依据冶金过程中的工艺要求建立了二冷水量优化模型,并在经典的PSO算法基础上提出了适合该问题求解了混合自适应PSO算法。由于连铸过程存在着偏微分方程约束,传统的优化方法容易陷入局部最优解,不能达到很好的动态优化效果。研究了粒子群算法,基于种群的多样性,不断的自适应的更新粒子群算法中参数,将禁忌搜索的方法和传统的粒子群算法结合,增强了算法的局部搜索能力和全局寻找全局最优的能力。将该算法应用到连铸二冷水动态优化中,实验结果表面该算法能够快速有效的求解该优化问题。该方法用于连铸二冷水优化是可行的、有效的。     

大尺度分层介质电特性参数的反演方法研究

针对大尺度分层介质电磁场散射回波数据, 提出了一种用于分层介质特性参数反演的新方法, 实现了分层介质的介电常数、电导率和介质厚度的同时反演. 方法首先将这些参数的反演转化为最优化问题进行求解, 然后应用模拟退火算法搜索最优解, 充分利用算法的全局寻优能力, 同时改善了算法的搜索控制策略, 使算法在最优解的搜索过程中可以自适应地调整搜索步长, 提高了算法的搜索效率. 结果表明, 本文方法可以实现大尺度分层介质特性参数的准确反演, 且具有较强的抗噪声能力. 该方法可以应用于火星/月球 雷达探测回波数据的反演以及地下分层结构特性的分析. 关键词： 电磁散射 分层介质 模拟退火算法 最优化     

改进退火遗传算法在SQL数据挖掘参数优化中的应用

针对SQL数据挖掘在复杂动力学系统故障诊断中的模式分类问题,以决策树参数优化为例,开展SQL数据挖掘分类算法参数优化研究。目前数据挖掘中的各类算法参数往往根据经验值设定,预测精度不高;只用遗传算法进行参数优化,分类预测结果容易发生振荡和早熟现象。采用改进的退火遗传算法对SQL数据挖掘中的决策树算法参数进行优化,解决了人工经验设置参数效率低下、精度不高的问题,同时实现了全局搜索,快速收敛到全局最优解。     

基于遗传模拟退火算法的室内照明节能控制方法研究北大核心CSCD

目前的节能照明控制算法仍有陷入局部最优的问题。为了寻求全局最优解,提高室内照明的节能效果,设计一种遗传模拟退火算法对照明系统的控制参数进行优化求解。该算法通过在遗传操作后对优秀个体进行模拟退火处理,增强了算法的局部搜索能力。根据迭代的次数和种群的适应度对遗传概率进行自适应调节,使得算法在前期丰富种群多样性,避免算法“早熟”。提出基于人工神经网络的照度模型来计算室内照度分布,对照明舒适度进行评估,为构造优化算法的适应函数提供了依据。通过仿真实验,在本文介绍的照明场景应用遗传模拟退火算法,并与传统粒子群算法和遗传算法进行比较,其照明节能性能分别高出5.30%和13.61%。     

基于遗传模型改进蜂群算法的稀疏阵列优化

人工蜂群算法作为一种新兴的群体智能算法,在解决复杂连续问题时表现突出。但是由于算法本身内在运行机制的原因,算法在搜索上表现出优异的性能,却疏于开发。为了平衡搜索和开发二者之间的矛盾,提出了一种基于遗传模型改进的人工蜂群算法,并成功运用到了阵列综合领域。算法先将全局最优解引入邻域搜索过程,指导蜂群寻找最佳蜜源,加速算法收敛。为了避免人工蜂群算法陷入局部最优,需要提高其开发能力,通过借鉴遗传算法中的进化机制,建立了遗传模型,对采取最佳保留后的蜜源进行遗传操作,丰富蜜源的多样性。在一组广泛使用的数值函数上对改进人工蜂群算法进行了测试,实验数据表明,该算法相较于其他算法具有很强的竞争力。将该算法运用于线性阵列的稀疏优化,旨在降低阵列的峰值旁瓣电平,在同样的阵列约束下与其他算法进行了优化对比,仿真结果进一步证明了算法的有效性。     

Truss optimization on shape and sizing with frequency constraints based on orthogonal multi-gravitational search algorithm

Structural optimization on shape and sizing with frequency constraints is well-known as a highly nonlinear dynamic optimization problem with several local optimum solutions. Hence, efficient optimization algorithms should be utilized to solve this problem. In this study, orthogonal multi-gravitational search algorithm (OMGSA) as a meta-heuristic algorithm is introduced to solve truss optimization on shape and sizing with frequency constraints. The OMGSA is a hybrid approach based on a combination of multi-gravitational search algorithm (multi-GSA) and an orthogonal crossover (OC). In multi-GSA, the population is split into several sub-populations. Then, each sub-population is independently evaluated by an improved gravitational search algorithm (IGSA). Furthermore, the OC is used in the proposed OMGSA in order to find and exploit the global solution in the search space. The capability of OMGSA is demonstrated through six benchmark examples. Numerical results show that the proposed OMGSA outperform the other optimization techniques.     

采用三层保护策略的强制进化随机游走算法同步综合换热网络

强制进化随机游走算法（RWCE）同步综合换热网络时,存在个体最优解的进化路径被接受差解打乱而不接受差解又很难跳出局部最优的问题.提出一种采用三层保护策略的RWCE算法,将种群中个体分为三层,底层采用基本RWCE进行优化,以保护个体的全局搜索能力;中层读取底层各个体的历史最优解,并采用带微调功能的RWCE进行优化,以保护各个体最优解的进化路径不被打乱;顶层所有个体以中层最优个体的解为初始点,采用带自动精细搜索功能的RWCE进行优化,以保证最优个体得到充分的搜索;最后将顶层搜索到的结果传递给底层对应个体.实例表明,算法在允许接受差解的同时保护了个体最优解的进化路径,并实现了全局搜索能力与局部搜索能力的兼顾.     

A Hybrid Genetic-Hierarchical Algorithm for the Quadratic Assignment Problem

In this paper, we present a hybrid genetic-hierarchical algorithm for the solution of the quadratic assignment problem. The main distinguishing aspect of the proposed algorithm is that this is an innovative hybrid genetic algorithm with the original, hierarchical architecture. In particular, the genetic algorithm is combined with the so-called hierarchical (self-similar) iterated tabu search algorithm, which serves as a powerful local optimizer (local improvement algorithm) of the offspring solutions produced by the crossover operator of the genetic algorithm. The results of the conducted computational experiments demonstrate the promising performance and competitiveness of the proposed algorithm.     

基于微种群遗传算法和自适应BP算法的遥感图像分类

介绍了采用微种群遗传算法和自适应BP算法相结合的混合遗传算法来训练前向人工神经网络(BPNN)的方法。即先用微种群遗传学习算法进行全局训练,再用自适应BP算法进行精确训练,以达到加快网络收敛速度和避免陷入局部极小值的目的。将此算法用于遥感图像分类,网络的训练速度及分类结果表明,该算法收敛速度较快,预测精度较高。