Rational approximation for fractional-order system by particle swarm optimization

In this paper, a rational approximation me-thod is proposed for the fractional-order system using the particle swarm optimization (PSO). Firstly, the approximation method for the fractional-order operator is studied, because a fractional-order system consists of many fractional-order operators. The coefficients of the transfer function are calculated using PSO with a fitness function under the continued fraction expansion (CFE) framework in the frequency domain. The average velocity of the particle swarm is defined to reflect the real state of particle swarm. To improve the global optimization and achieve a more satisfactory fitting result, comparing with the linear PSO, the chaotic optimization is combined with PSO. The numerical examples of fractional-order systems demonstrate the effectiveness of this method.     

动基座平台式制导工具误差分离的粒子群算法

针对动基座发射飞行器的制导工具误差分离是一个不适定问题,采用经典参数的粒子群算法(PSO)分离制导工具误差和初始误差。目标函数考虑4个因素构建:①逼近遥外差的残差;②地面测试值先验信息;③初始误差的直接或者间接测量信息;④估计结果符合天地一致性的个数。这四个因素充分利用了误差分离前可以利用的有效信息,并利用有效信息提取先验信息。通过构建目标函数,利用先验信息把误差项分为严格有界项和允许越界项。算例表明,所提出的算法优于其他算法,在逼近遥外差的残差足够小的同时,估计结果符合天地一致性的项数为26项,比主成分贝叶斯估计结果多8项。     

Chaotic ant swarm optimization with passive congregation

Chaotic ant swarm optimization (CASO) is a powerful chaos search algorithm for optimization problems, but it is often easy to be premature convergence. To overcome the weakness, this paper presents a CASO with passive congregation (CASOPC). Passive congregation is one type of biological information sharing mechanisms that allow animals to aggregate into groups and help to enhance the exploitation of animals. By introducing passive congregation strategy into the CASO, a modified evolution equation based on the CASO is proposed in the CASOPC. The modified evolution equation cannot only employ the parallel search of all ants and the well exploration ability of the CASO, but also stress and control the exploitation by passive congregation coefficient c in the stage of evolution. Due to linearly increasing c in the CASOPC, the exploration and exploitation ability of ants are well balanced so that premature convergence can be avoided and good performance can be achieved. In order to estimate the capability of the CASOPC, it is tested with a set of 5 benchmark functions with 30 dimensions and compared to the CASO. Experimental results indicate that the CASOPC improves the search performance on the benchmark functions significantly.     

Particle interaction in a flow with a parabolic velocity profile

The hydrodynamic interaction of two rigid spherical particles in a viscous incompressible fluid with the velocity at infinity represented by a second-degree polynomial in the coordinates is considered. An analytical solution of the problem is suggested. The forces and torques exerted on the particles and also the linear and angular particle velocities are calculated. The results are compared with previous theoretical and experimental data. Saransk. Translated from Izvestiya Rossiiskoi Akademii Nauk, Mekhanika Zhidkosti i Gaza, No. 1, pp. 84–91, January–February, 2000. The work received financial support from the Russian Foundation for Basic Research (project No. 98-01-03295).     

Robust airfoil optimization based on improved particle swarm optimization method

A robust airfoil optimization platform is constructed based on the modified particle swarm optimization method (i.e., the second-order oscillating particle swarm method), which consists of an efficient optimization algorithm, a precise aerodynamic analysis program, a high accuracy surrogate model, and a classical airfoil parametric method. There are two improvements for the modified particle swarm method compared with the standard particle swarm method. First, the particle velocity is represented by the combination of the particle position and the variation of position, which makes the particle swarm algorithm a second-order precision method with respect to the particle position. Second, for the sake of adding diversity to the swarm and enlarging the parameter searching domain to improve the global convergence performance of the algorithm, an oscillating term is introduced to the update formula of the particle velocity. At last, taking two airfoils as examples, the aerodynamic shapes are optimized on this optimization platform. It is shown from the optimization results that the aerodynamic characteristic of the airfoils is greatly improved in a broad design range.     

Particle tracking velocimetry with an ant colony optimization algorithm

A new concept algorithm based on the ant colony optimization is developed for the use in 2-D and 3-D particle tracking velocimetry (PTV). In the particle matching process of PTV, the ant colony optimization is usually aimed at minimization of the sum of the distances between the first-frame and second-frame particles. But this type of minimization often goes unsuccessfully in the regions where the particles are located very close to each other. In order to avoid this flaw, a new type of minimization is attempted using a physical property corresponding to the flow consistency or the quasi-rigidity of particle distribution patterns. Specifically, the ant colony optimization is now aimed at minimization of the sum of the relaxation of neighbor particles. In the present study, the new algorithm is applied to sets of 2-D and 3-D synthetic particle images as well as the experimental images with successful results.     

基于改进粒子群算法的翼型多目标优化研究

在多目标优化研究中,为改善多目标粒子群算法的局部搜索能力,以标准粒子群算法为基础,引入单点模拟退火算法,局部进化最优个体,采用基于目标向量的共享函数法评价适应值.标准测试函数优化实例表明:本文算法比标准粒子群算法具有更好的收敛稳定性和收敛速度,收敛速度提高了近50%;针对某翼型的气动优化设计结果表明:改进算法有效缩短了优化时间,迭代代数由61减为49,调用CFD由4880减为4250次;阻力系数、升力系数、低头力矩系数分别改进了9.23%、0.42%、16.4%,取得了较好的优化效果.     

小当量地下爆炸的质点速度模型

在第四纪砂砾层中进行了系列公斤至百公斤级的地下封闭爆炸,研究了小当量化学爆炸地震波传播规律。在近场,地震波持时很短,水平振幅强于垂向振幅。质点速度随当量呈现指数增加,水平向指数为1.09,垂直向指数为0.77,质点速度随距离呈现指数衰减,水平向和垂直向指数分别为2.07与1.57。Sadauskas模型、显函模型和双极模型都能定量描述小当量地下爆炸地震波质点速度变化,但他们之间的反演精度存在差异,双极模型的残差最小。换言之,采用双极模型反演的数据更接近实际。     

A particle swarm optimization approach for hexahedral mesh smoothing

There are some approaches for all‐hexahedral mesh quality improvement by means of node‐movement while preserving the connectivity. Among these methods, the most easily implemented and well known one is the Laplacian smoothing method; however, for this method mesh quality improvement is not guaranteed in all cases, and this approach might cause inverted elements especially in concave regions. In this work, a method for the improvement of hexahedral mesh shape‐quality without causing inverted elements is proposed; which is based on optimization of an objective function calculated by means of the individual qualities of hexahedral elements in the mesh. The shape‐quality for each hexahedral element is defined via the condition number of the relevant element. The numerical optimization scheme is the particle swarm optimization method, which originated from observations related to the social behaviors of bird, insect, or fish colonies. The purpose of this paper is to discuss the applicability of this approach to mesh smoothing. Some examples are given in order to demonstrate the applicability. Copyright © 2008 John Wiley & Sons, Ltd.     

粒子群优化算法在传递对准中的应用

给出了一种基于粒子群优化算法的捷联惯导传递对准算法。简单分析了传递对准任务要求和主子惯导惯性器件输出之间的关系,将传递对准问题作为参数优化问题进行求解,给出了基于粒子群优化算法进行传递对准的数学模型。定义了传递对准的优化目标函数,介绍了粒子群优化算法及其应用于传递对准的具体算法设置。用粒子群优化算法求解目标函数的最小值,可获得主子惯导之间的失准角,进行一次校正即可完成传递对准过程。通过计算机仿真对算法进行了验证分析,在仿真条件下（陀螺精度为0.1°/h）,能达到方位0.1°的精度。与其他对准算法一样,算法受载体机动条件的影响较大,一般需要姿态机动来提高陀螺的信噪比。     

Robust adaptive fractional-order observer for a class of fractional-order nonlinear systems with unknown parameters

Nonlinear Dynamics - This paper investigates the parameter and state estimation problems for a class of fractional-order nonlinear systems subject to the perturbation on the observer gain. The...     

基于微粒群算法优化支持向量机的加速度计静态模型辨识

针对加速度计静态模型采用线性近似模型辨识存在较大误差的问题,利用支持向量回归在小样本、非线性及高维特征空间中具有很好的推广能力的优点,提出了一种利用支持向量回归进行加速度计静态模型辨识的方法.为了避免随机试凑法识别支持向量回归参数费时的问题,采用高效的并行搜索算法-微粒群算法进行支持向量回归参数优化.利用精密光学分度头对石英挠性加速度计进行了12位置静态翻滚试验,试验结果表明所提方法可以精确地对石英挠性加速度计静态模型进行辨识,其模型精度比最小二乘辨识法的模型精度提高一倍以上.     

Secure communication on fractional-order chaotic systems via adaptive sliding mode control with teaching–learning–feedback-based optimization

Nonlinear Dynamics - Due to the significance of secure communication, we do a research about this problem based on fractional-order chaotic systems, where a communication scheme is presented for...     

Anomaly detection combining one-class SVMs and particle swarm optimization algorithms

Anomalies are patterns in data that do not conform to a well-defined notion of normal behavior. One-class Support Vector Machines calculate a hyperplane in the feature space to distinguish anomalies, but the false positive rate is always high and parameter selection is a key issue. So, we propose a novel one-class framework for detecting anomalies, which takes the advantages of both boundary movement strategy and the effectiveness of evaluation algorithm on parameters optimization. First, we search the parameters by using a particle swarm optimization algorithm. Each particle suggests a group of parameters, the area under receiver operating characteristic curve is chosen as the fitness of the object function. Second, we improve the original decision function with a boundary movement. After the threshold has been adjusted, the final detection function will bring about a high detection rate with a lower false positive rate. Experimental results on UCI data sets show that the proposed method can achieve better performance than other one class learning schemes.     

基于粒子群-AK-MCS法的结构可靠性分析

Kriging代理模型由于其良好的非线性拟合能力,在可靠性分析领域得到了广泛的应用。为提高Kriging模型的建模效率,本文提出一种混合粒子群算法的AK-MCS法,该方法在保证模型精度的前提下减少了构建代理模型时的迭代次数,且提高了模型的全局寻优能力。以一10杆桁架结构为研究对象,通过建立结构控制位移与杆件截面面积和集中荷载之间的代理模型,快速求解桁架结构的失效概率。研究表明,本文方法有效提高了Kriging模型建模效率和建模精度,在计算复杂工程结构时具有一定的可行性。     

基于改进粒子群优化的水下地形辅助导航方法

为了提高传统地形匹配算法的定位精度,提出一种基于改进粒子群优化的水下地形辅助导航定位算法。该算法以SINS的指示位置为中心构造搜索区域,对二维粒子群进行初始化,利用实时水深测量序列与待匹配序列之间的平均Hausdorff距离作为适应度函数,在线性递减权重的基础上引入收敛因子对粒子的速度和位置进行约束更新,改善粒子"早熟"问题。在某海图内进行了水下地形匹配仿真实验,结果表明:初始位置误差大小不影响改进PSO算法的定位精度和匹配速度;当水下航行器初始位置误差较大时,与TERCOM算法相比,改进PSO算法的匹配精度提高了近5倍,匹配耗时缩减了近10倍。     

Stacking sequence optimization of composite plates for maximum fundamental frequency using particle swarm optimization algorithm

The paper illustrates the application of the particle swarm optimization (PSO) algorithm to the lay-up design of symmetrically laminated composite plates for maximization of fundamental frequency. The design variables are the fiber orientation angles, edge conditions and plate length/width ratios. The formulation is based on the classical laminated plate theory (CLPT), and the method of analysis is the semi-analytical finite strip approach which has been developed on the basis of full energy methods. The performance of the PSO is also compared with the simple genetic algorithm and shows the good efficiency of the PSO algorithm. To check the validity, the obtained results are compared with those available in the literature and some other stacking sequences, wherever possible.     

Stability of fractional-order systems with Prabhakar derivatives

Nonlinear Dynamics - Fractional derivatives of Prabhakar type are capturing an increasing interest since their ability to describe anomalous relaxation phenomena (in dielectrics and other fields)...     

Thermal-economic multi-objective optimization of shell and tube heat exchanger using particle swarm optimization (PSO)

Many studies are performed by researchers about shell and tube heat exchanger (STHE) but the multi-objective particle swarm optimization (PSO) technique has never been used in such studies. This paper presents application of thermal-economic multi-objective optimization of STHE using PSO. For optimal design of a STHE, it was first thermally modeled using e-number of transfer units method while Bell–Delaware procedure was applied to estimate its shell side heat transfer coefficient and pressure drop. Multi objective PSO (MOPSO) method was applied to obtain the maximum effectiveness (heat recovery) and the minimum total cost as two objective functions. The results of optimal designs were a set of multiple optimum solutions, called ‘Pareto optimal solutions’. In order to show the accuracy of the algorithm, a comparison is made with the non-dominated sorting genetic algorithm (NSGA-II) and MOPSO which are developed for the same problem.     

Particle swarm optimization-based algorithm of a symplectic method for robotic dynamics and control

Multibody system dynamics provides a strong tool for the estimation of dynamic performances and the optimization of multisystem robot design. It can be described with differential algebraic equations(DAEs). In this paper, a particle swarm optimization(PSO) method is introduced to solve and control a symplectic multibody system for the first time. It is first combined with the symplectic method to solve problems in uncontrolled and controlled robotic arm systems. It is shown that the results conserve the energy and keep the constraints of the chaotic motion, which demonstrates the efficiency, accuracy, and time-saving ability of the method. To make the system move along the pre-planned path, which is a functional extremum problem, a double-PSO-based instantaneous optimal control is introduced. Examples are performed to test the effectiveness of the double-PSO-based instantaneous optimal control. The results show that the method has high accuracy, a fast convergence speed, and a wide range of applications.All the above verify the immense potential applications of the PSO method in multibody system dynamics.