基于粒子群算法的稀疏阵列超声相控阵全聚焦成像

为降低相控阵超声检测全聚焦算法的成像数据量及阵列稀疏优化的计算时间,研究了一种用于稀疏阵列全聚焦成像的阵列优化算法,并通过实验对其成像效果进行了验证。针对目前超声相控阵检测的全矩阵采集数据量大、全聚焦算法成像时间长的难点,该文通过构建稀疏阵列,在保证成像质量的同时显著降低成像数据量,提高了全聚焦算法的成像效率。通过以主瓣宽度、旁瓣峰值以及主瓣峰值作为约束条件构建适应度函数,采用粒子群算法得到稀疏阵元位置分布并进行阵元权重修正,并将其用于稀疏全聚焦成像。相比全阵元成像,使用粒子群算法所得的稀疏阵列的阵元个数降低了56.25%、65.62%,数据使用量降低了80.86%、88.18%。在阵列优化方面,相比遗传算法减少了84.86%的计算时间。     

An experiment for obtaining DOP ellipsoid using particle swarm optimization algorithm

The degree of polarization (DOP) ellipsoid can be used as either feedback or feedforward signal for automatic polarization mode dispersion compensation. We have realized the experiment for obtaining DOP ellipsoid from 100 sampling data of output states of polarization using particle swarm optimization (PSO) as ellipsoid data fitting algorithm. It was shown that the PSO algorithm was powerful for ellipsoid data fitting with high precision within 250 ms.     

An experiment for obtaining DOP ellipsoid using particle swarm optimization algorithm

The degree of polarization (DOP) ellipsoid can be used as either feedback or feedforward signal for automatic polarization mode dispersion compensation. We have realized the experiment for obtaining DOP ellipsoid from 100 sampling data of output states of polarization using particle swarm optimization (PSO) as ellipsoid data fitting algorithm. It was shown that the PSO algorithm was powerful for ellipsoid data fitting with high precision within 250 ms.     

High-pressure structures of InBi predicted by particle swarm optimization algorithm

We extensively explore the high-pressure structures of InBi by using a newly developed particle swarm optimization algorithm. An orthorhombic Imma structure is discovered to be stable from 43.7 GPa to 107.9 GPa, ruling out the previously speculated cubic structure. Further increasing the pressure, we find a tetragonal P4/nmm structure which is energetically more favourable from 107.9 GPa to 200 GPa. Especially, the tetragonal P4/nmm structure is known to occur at high pressure in the structures of ZnO and MgTe. We also predict this structure to be a high-pressure structure of ZnTe. Thus the tetragonal P4/nmm structure may be a universal high-pressure structure of the Ⅱ-Ⅵ and the Ⅲ-Ⅴ compounds.     

基于改进粒子群优化算法的氩原子团簇结构优化

采用改进粒子群优化算法(IPSO)结合Lennard-Jones势对氩原子团簇结构进行优化,得到了氩原子团簇的稳态结构能量。以氩原子团簇Arn（n = 2-14）为例,验证了该方法的有效性。结果表明,应用本文提出的方法可得到对称性良好的团簇结构。与基本粒子群优化算法(BPSO)及遗传算法(GA)相比,改进粒子群优化算法具有更好的收敛特性,能较快地得到氩原子团簇结构的最优解。     

微动粒子群优化算法用于Egun的多参量优化

针对2维电子光学多参量优化问题,采用微动粒子群优化算法,在给出目标电子轨迹和优化范围的前提下,可以得到趋近于该电子轨迹的真空边界和聚束磁结构。该算法分为前后两阶段：第一阶段采用前后试探法(微动),同时参照最优粒子的信息;第二阶段采用标准粒子群优化算法。针对涉及多个相关参量的电子光学设计问题,标准粒子群优化算法仅能保证以较高概率收敛到局部最佳解,而微动粒子群优化算法能以较高概率收敛到全局最佳解,并且展现了多核计算机在电子光学设计上的潜力。初步的软件试验显示：消耗人类工程师几周时间的电子光学设计问题,用微动粒子群算法在普通个人计算机上几十小时就能完成。     

A robust SVD-based image watermarking using a multi-objective particle swarm optimization

The major objective in developing a robust digital watermarking algorithm is to obtain the highest possible robustness without losing the visual imperceptibility. To achieve this objective, we proposed in this paper an optimal image watermarking scheme using multi-objective particle swarm optimization (MOPSO) and singular value decomposition (SVD) in wavelet domain. Having decomposed the original image into ten sub-bands, singular value decomposition is applied to a chosen detail sub-band. Then, the singular values of the chosen sub-band are modified by multiple scaling factors (MSF) to embed the singular values of watermark image. Various combinations of multiple scaling factors are possible, and it is difficult to obtain optimal solutions. Thus, in order to achieve the highest possible robustness and imperceptibility, multi-objective optimization of the multiple scaling factors is necessary. This work employs particle swarm optimization to obtain optimum multiple scaling factors. Experimental results of the proposed approach show both the significant improvement in term of imperceptibility and robustness under various attacks.     

基于粒子群优化的混沌系统比例-积分-微分控制

基于比例-积分-微分(PID)控制算法的简单性和实用性,但对于复杂非线性系统控制时参数的难以确定问题,运用集群智能中的改进粒子群算法进行PID控制器的优化,并应用于若干混沌系统的控制.对Hénon混沌、Duffing混沌、六辊UC 轧机混沌、Nagumo-sato神经元混沌、Chen氏混沌以及永磁同步电动机混沌的控制进行了仿真研究.研究结果表明： 用PID进行混沌系统的输出反馈控制是有效的,从而拓宽了PID控制的应用范围; 用简单方法控制复杂混沌系统是完全可能的,对混沌系统的控制具有较好的参考价值; 粒子 关键词： 混沌 比例-积分-微分控制 粒子群优化算法     

用粒子群优化算法设计光纤布拉格光栅

用粒子群优化(PSO)算法对二元相位取样光栅进行优化设计.实现了利用二元相位取样光栅对多信道色散和色散斜率的同时补偿.设计得到的二元相位取样光栅的反射谱各信道均匀,时延线性好,带宽、色散量和色散斜率可以根据实际的光纤类型来调整,对折射率调制要求较低.由于只有π相移,因此其制作比多级相位取样光栅相对简单.与其他优化方法相比,粒子群优化算法具有简单、收敛速度快等优点.     

用粒子群算法校正三片镜系统的像差（特邀）

提出将粒子群算法用于三片镜光学系统的优化设计。设计了关于曲率半径、透镜面之间的距离、玻璃折射率、系统长度等光学系统结构参数的光学评价函数,用此函数作为粒子群算法中的适应度函数,实现了对光学系统的自动寻优。给出了用粒子群算法进行三片镜光学系统设计过程实例,结果证明：用粒子群算法可以设计出球差、子午场曲、子午光线弥散值都很小的三片镜光学系统;并且用该算法进行光学设计不需要知道系统具体的初始结构,克服了现有光学设计软件高度依赖具体初始结构的缺点,可以自由控制结构参数的搜索范围,从而提高光学系统设计的智能化程度。     

2维静磁场多参量微动粒子群优化算法

提出一种微动粒子群优化算法,针对2维静磁场多参量优化问题,在给出轴上目标轴向磁感应强度分布曲线的前提下,可以得到趋近于该分布曲线的磁结构。该算法分为前后两阶段:第一阶段采用前后试探法(微动),同时也参照最优粒子的信息;第二阶段采用基本粒子群优化算法。微动粒子群优化算法可以发挥多核计算机在工程设计上的潜力,而且即使粒子数目很少,也能不断趋近目标解。     

Modeling of pedestrian evacuation based on the particle swarm optimization algorithm

By applying the evolutionary algorithm of Particle Swarm Optimization (PSO), we have developed a new pedestrian evacuation model. In the new model, we first introduce the local pedestrian’s density concept which is defined as the number of pedestrians distributed in a certain area divided by the area. Both the maximum velocity and the size of a particle (pedestrian) are supposed to be functions of the local density. An attempt to account for the impact consequence between pedestrians is also made by introducing a threshold of injury into the model. The updating rule of the model possesses heterogeneous spatial and temporal characteristics. Numerical examples demonstrate that the model is capable of simulating the typical features of evacuation captured by CA (Cellular Automata) based models. As contrast to CA-based simulations, in which the velocity (via step size) of a pedestrian in each time step is a constant value and limited in several directions, the new model is more flexible in describing pedestrians’ velocities since they are not limited in discrete values and directions according to the new updating rule.     

2维静磁场多参量微动粒子群优化算法

提出一种微动粒子群优化算法,针对2维静磁场多参量优化问题,在给出轴上目标轴向磁感应强度分布曲线的前提下,可以得到趋近于该分布曲线的磁结构。该算法分为前后两阶段:第一阶段采用前后试探法(微动),同时也参照最优粒子的信息;第二阶段采用基本粒子群优化算法。微动粒子群优化算法可以发挥多核计算机在工程设计上的潜力,而且即使粒子数目很少,也能不断趋近目标解。     

Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization

Blasting is an inseparable part of the rock fragmentation process in hard rock mining. As an adverse and undesirable effect of blasting on surrounding areas, airblast-overpressure (AOp) is constantly considered by blast designers. AOp may impact the human and structures in adjacent to blasting area. Consequently, many attempts have been made to establish empirical correlations to predict and subsequently control the AOp. However, current correlations only investigate a few influential parameters, whereas there are many parameters in producing AOp. As a powerful function approximations, artificial neural networks (ANNs) can be utilized to simulate AOp. This paper presents a new approach based on hybrid ANN and particle swarm optimization (PSO) algorithm to predict AOp in quarry blasting. For this purpose, AOp and influential parameters were recorded from 62 blast operations in four granite quarry sites in Malaysia. Several models were trained and tested using collected data to determine the optimum model in which each model involved nine inputs, including the most influential parameters on AOp. In addition, two series of site factors were obtained using the power regression analyses. Findings show that presented PSO-based ANN model performs well in predicting the AOp. Hence, to compare the prediction performance of the PSO-based ANN model, the AOp was predicted using the current and proposed formulas. The training correlation coefficient equals to 0.94 suggests that the PSO-based ANN model outperforms the other predictive models.     

量子势阱粒子群优化算法的改进研究

为提高量子势阱粒子群优化算法的优化能力, 通过分析目前量子势阱粒子群优化算法的设计过程, 提出了改进的量子势阱粒子群优化算法. 首先, 分别基于Delta势阱、谐振子和方势阱 提出了改进的量子势阱粒子群优化算法, 并提出了基于统计量均值的控制参数设计方法. 然后, 在势阱中心的设计方面, 为强调全局最优粒子的指导作用, 提出了基于自身最优粒子加权平均和动态随机变量的两种设计策略. 实验结果表明, 三种势阱粒子群优化算法性能比较接近, 都优于原算法, 且Delta势阱模型略优于其他两种.     

An evolutionary particle swarm optimization algorithm for mathematical processing of experimental spectra

A particle swarm optimization algorithm is applied for mathematical treatment of Fourier-transform IR spectra of branched copolymers of methyl methacrylate. The efficiency of reconstruction of the spectra using the particle swarm optimization algorithm as compared with the least squares method is illustrated by the example of decomposition of a six-component experimental spectrum.     

Design of broadband hybrid fiber amplifier based on particle swarm optimization algorithm北大核心CSCD

基于铒/镱共掺光纤放大器（erbium-ytterbium doped fiber amplifier, EYDFA）的理论模型和受激拉曼散射效应的分析理论,利用EYDFA和拉曼光纤放大器（Raman fiber amplifier, RFA）的增益谱互补特性,研究并设计了EYDFA与二阶多泵浦RFA相结合的混合放大器结构。为了得到高增益和低平坦度的混合放大器,引入了粒子群算法优化泵浦光波长和功率。仿真结果表明:在不使用增益均衡器的条件下,所设计的混合光纤放大器在输出端得到了近似相等的输出光功率,在90 nm的带宽范围内平均增益为38.78 dB,增益平坦度为1.1 dB,为混合放大器的设计和优化提供了参考。     

基于粒子群优化支持向量机的太阳电池温度预测

建立通用而精确的太阳电池热模型对光伏系统的建模、输出功率与转换效率的损失分析至关重要. 基于复杂的太阳电池温度机理, 分别研究了太阳电池温度的稳态热模型(steady state thermal model, SSTM)和支持向量机(support vector machines, SVM) 方法建立的精确预测热模型. 首先, 基于空气温度、太阳辐射强度、风速3个最主要因素与太阳电池温度的近似线性关系, 在已有SSTM的基础上, 建立并校正了太阳电池的SSTM并采用差分进化算法提取模型的未知参数. 其次, 为提高SVM的模型预测精度, 采用粒子群优化(particle swarm optimization, PSO) 算法对SVM的核参数和惩罚因子进行动态寻优, 在确定输入/输出样本集并划分训练集和测试集的基础上, 建立了基于粒子群优化支持向量机(PSO-SVM)的太阳电池温度精确预测热模型. 最后, 搭建实验平台, 在实验操作过程中减弱空气湿度、太阳入射角和热迟滞效应等因素对太阳电池温度的耦合. 通过实验对比表明, 建立的预测热模型性能可靠、全面、简洁, 其参数寻优算法优于遗传算法和交叉校验法, 模型预测精度优于反向传播神经网络(back propagation neural network) 和SSTM.