Application of the combined CFD and swarm intelligence for optimization of baffles number in a mixer-settler

Process optimization in a mixer-settler is of great importance. Optimization algorithm of particle swarm optimization is one of the evolutionary algorithms to solve optimization problem which is used in many fields. In this study, the optimal condition is calculated in finite volume method in terms of the number of baffles, inlet velocity of fluid, and impeller speed in a mixer-settler with a specific dimension that can be extended to industrial dimensions using the PSO algorithm and the numerical solution of Navier-Stokes equations and k-ε standard.     

广东省能源需求预测模型构建及实证分析

为了对广东省的能源需求进行准确的预测,首先分析了影响广东省能源需求的各种因素,构建了预测指标体系.在此基础上,针对能源系统非线性等复杂系统特征,结合粒子群算法和BP神经网络的优点,构建了改进的PSO-BP神经网络的预测模型,并通过主成分分析法对指标体系进行数据降维,以降低神经网络的规模和复杂程度.以广东省1985-2013年的能源需求数据进行模拟与仿真,并对2014-2018年的能源需求量进行预测,理论分析和实证研究表明,该方法能够很好的反映广东省能源需求的特征,预测结果较为准确合理.     

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。     

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针对强磁场实验装置(SHMFF)的链式高压有源电力滤波方案规划,基于有源滤波系统的非线性特性, 采用分数阶值。PI 控制器替代普通PI 控制器,在此基础上引入粒子群算法优化指令电流PI 控制器参数,并通过 实验对比分析了人工整定的PI 控制器与PSO 整定的PI 控制器的运行效果。经样机实验表明,采用的PSO 算法 整定的电流内环控制器是正确的,且具有一定的参考价值。     

优化MGM(1,n)模型及其应用研究

从最小二乘估计的适用条件出发,在建立MGM(1,n)模型前,先采用BoxCox变换对原始数据进行变换,以解决或缓解误差项的正态性偏离问题.首先,以模型拟合平均误差最小化为目标,建立非线性优化模型,在参数的置信区间内,应用PSO(Particle Swarm Optimization)算法求最优解.然后,应用求解得到的参数,建立优化MGM(1,n)模型.在实例分析中,将优化MGM(1,n)模型应用于深基坑围护结构变形预测,实验结果表明通过对原始数据做合适的Box-Cox变换,能够有效提高模型的拟合及预测精度,拓广多变量灰色预测模型的适用范围.     

Initial guess by improved population-based intelligent algorithms for large inter-frame deformation measurement using digital image correlation

Digital image correlation (DIC) has received a widespread research and application in experimental mechanics. In DIC, the performance of subpixel registration algorithm (e.g., Newton-Raphson method, quasi-Newton method) relies heavily on the initial guess of deformation. In the case of small inter-frame deformation, the initial guess could be found by simple search scheme, the coarse-fine search for instance. While for large inter-frame deformation, it is difficult for simple search scheme to robustly estimate displacement parameters and deformation parameters simultaneously with low computational cost. In this paper, we proposed three improving strategies, i.e. Q-stage evolutionary strategy (T), parameter control strategy (C) and space expanding strategy (E), and then combined them into three population-based intelligent algorithms (PIAs), i.e. genetic algorithm (GA), differential evolution (DE) and particle swarm optimization (PSO), and finally derived eighteen different algorithms to calculate the initial guess for qN. The eighteen algorithms were compared in three sets of experiments including large rigid body translation, finite uniaxial strain and large rigid body rotation, and the results showed the effectiveness of proposed improving strategies. Among all compared algorithms, DE-TCE is the best which is robust, convenient and efficient for large inter-frame deformation measurement.     

基于粒子群算法的面向节能的单/多列车优化决策问题

针对列车运行优化控制问题,首先对列车运行过程的受力情况进行分析,建立了列车运行模型;接着根据最优惰行点选择原则和再生制动节能的方法,确立了单列车区间运行优化模型与多列车节能运行模型;然后采用基于高斯白噪声扰动变异的粒子群算法(GMPSO)求解上述模型得出各情况下列车的最优控制策略;最后探讨了列车延迟情况下的节能调整方案.     

基于粒子群优化算法的船舶动力定位云模型控制器设计

云模型控制理论是智能控制学科的新兴领域,因此如何扩展云模型的应用范围并使其走向工程化实用化成为其研究重点。针对船舶运动模型具有不确定性和外部扰动随机性等特点,尝试将云模型应用于船舶动力定位的控制过程中。由于云模型控制器存在参数难以整定的问题,提出了基于粒子群算法的优化设计方法。针对标准粒子群优化算法容易出现早熟收敛的问题,引入自适应粒子群优化算法。仿真研究表明云模型控制及粒子群优化的可行性和有效性。     

A hybrid Particle Chemical Reaction Optimization for biological image matching based on lateral inhibition

In this paper, a hybrid Particle Chemical Reaction Optimization (PCRO) algorithm and lateral inhibition is proposed to solve the image matching problem. Lateral inhibition has the ability to enhance the characters of image, which can help to improve the accuracy of image matching. In order to overcome the shortcomings of basic Chemical Reaction Optimization (CRO) algorithm, we improve CRO by proposing PCRO which inspired from the thought of Particle Swarm Optimization (PSO). Comparative experimental results in image matching show that our proposed hybrid method performs much better than other bio-inspired algorithms.     

基于多尺度自适应阶ARMA的混沌序列多步预测

混沌时间序列在自然界以及人们的生产生活中很常见,混沌序列看似杂乱无章但相较于纯随机序列其中蕴含着一些非线性的运动特征,提出一种基于多尺度自适应阶ARMA的混沌时间序列多步预测方法.首先利用自适应噪声的完备经验模态分解(CEEMDAN)对原始混沌序列进行分解,获得不同尺度的固有模态分量(IMF)和残余分量.然后采用经粒子群算法(PSO)进行阶数寻优的自回归移动平均模型(ARMA)对每一个IMF分量进行拟合预测.最后将预测得到的每一个分量相加得到原始混沌序列的预测值.基于Mackay-Glass混沌序列和太阳黑子数混沌序列进行实验分析,实验表明:与ARMA、PSO-ARMA以及CEEMDAN-ARMA方法相比,方法的预测效果有较好的提高,其平均绝对误差(MAE)、均方根误差(RMSE)以及平均绝对百分比误差(MAPE)都有降低.