基于PSO-HJ算法的多无人机协同航迹规划方法

针对多无人机协同航迹求解计算量大、难以收敛等问题,提出了一种基于粒子群优化和Hook-Jeeves (PSO-HJ)搜索算法相融合的多无人机时间协同三维航迹规划方法。首先,建立了单无人机航迹规划求解模型。然后,通过对适应度评价函数值低的粒子引入Hooke-Jeeves搜索算法,提高了粒子多样性,改善了航迹规划算法收敛性;对不满足约束的粒子引入约束违反度函数,基于比较准则提出了一种新的粒子评价机制,促进粒子搜索位于约束边界的最优解,加快了航迹规划算法的计算效率。最后,设计了一种多无人机时间协同航迹规划求解算法,利用PSO-HJ算法先分别求解单无人机航迹信息,通过多无人机集中航迹规划层协调到达时间实现协同航迹规划。仿真结果表明,PSO-HJ算法的精度比量子粒子群(QPSO)算法精度提高了20.85%,比PSO算法精度提高了58.14%,更适用解决实际复杂的多机协同规划问题。     

基于差分进化和RBF响应面的混合优化算法

针对气动优化等昂贵优化问题,提出了一种基于差分进化和RBF响应面的混合优化算法HSADE,该方法结合了差分进化算法的强全局寻优能力和RBF响应面方法的快速局部搜索能力,能够同时有效地提高算法的局部搜索效率和全局寻优能力.对各子算法中的策略和逻辑进行了多项改进,提出和应用了基于双败淘汰赛的竞赛赛制和参数自适应等改进策略.对HSADE使用多个典型算例进行了测试,并横向对比了NSGA-II,MOPSO和多目标差分进化算法.测试结果表明,在大多数问题中HSADE在以世代距离表征的局部搜索效率和以超体积比表征的全局寻优能力两项指标上都优于其他算法,证实了以上混合策略及算法改进的有效性.将该算法应用于一个翼型优化问题和一个二维超声速喷管膨胀面优化问题,并横向对比未经改良的差分进化算法DE和另一种混合算法NARSGA,结果表明在接近1 000次的函数评估下,HSADE能相对其他算法进一步对翼型减阻0.5 count,在喷管优化中HSADE得到的结果也好于其他两种算法,表明该方法具有较强工程应用价值.     

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。     

统一骨架与连续体的结构拓扑优化的ICM理论与方法

技术了基于ＩＣＭ方法的结构拓扑优化新模型并应用于骨架与连续体结构。ＩＣＭ方法意指独立、连续变量与映射及其反演。新模型将两种结构统一建立了具有重量目标函数和多工况下应力与位移约束下的优化问题,提出的过滤函数是ＩＣＭ方法的关键技术之一。说明了优化策略与算法。     

基于粒子群优化的UKF在SINS/GPS组合导航中的应用

针对组合导航中使用传统Kalman滤波方法时噪声协方差矩阵参数需要耗时耗力反复试验得到的问题,提出利用粒子群优化算法对卡尔曼滤波器的滤波参数Q和R进行寻优后用于组合导航的方法。将滤波参数Q和R作为粒子进行寻优,将粒子群算法优化得到的滤波参数值作为卡尔曼滤波器输入参数,用于SINS/GPS组合导航系统。仿真实验结果表明,12次实验中粒子群算法搜索出的参数均值分别为0.0208(°)/h、94.7827?g,接近所设置的噪声参数值与标准参数值0.02(°)/h、100?g。半物理实验结果表明,在实际系统中,与经验参数值用于卡尔曼滤波器相比,粒子群算法优化得到的滤波参数值位置估计精度提高了15%~30%,从而提高了组合导航性能。     

遗传-粒子群算法模型修正

用部分测量模态数据对5层钢架结构进行模型修正,将遗传算法、粒子群优化算法、 遗传-粒子群组合算法3种算法在该模型修正过程中的效率和精度进行比较,结果表明修正后 模型的全部四阶频率和振型都能在不同程度上向目标值靠近,证明3种算法都能够有效修正 模型,而且遗传-粒子群算法能在前期利用遗传算法进行高效全局搜索,后期利用粒子群算法 进行细致局部搜索,与单独使用遗传算法或粒子群算法相比,组合算法效率和精度更高.     

基于宏观调控策略的结构损伤检测PSO改进算法

结构损伤检测是结构健康监测过程重要的一步,数学上常常转化为求解约束优化问题。针对粒子群优化(PSO)算法易于出现的"早熟问题",采用市场经济条件下的宏观调控策略对早熟前粒子群位置进行干涉,藉以增强PSO算法抵抗局部极小的能力,达到改进PSO算法的目的。四个基准测试函数极值问题分析结果验证了改进后的PSO算法优于带权重因子的PSO算法,两层刚架单损伤和多损伤数值仿真以及三层建筑框架结构四种损伤工况试验研究进一步证明了改进后的PSO算法在结构损伤检测领域的应用是有效可行的。     

基于改进粒子群优化粒子滤波的结构损伤识别

针对粒子滤波应用于结构损伤识别问题时出现的粒子退化、反演计算强不适定性等现象,提出了一种改进的粒子群优化粒子滤波损伤识别方法。在粒子滤波算法中,利用粒子群优化过程驱使粒子群朝着后验概率密度取值较大的区域移动,优化了粒子滤波的采样过程;同时,根据结构损伤参数分布的稀疏性特点,引入对粒子群中损伤参数部分的零变异操作,既增加了粒子的多样性,又有效改善了反问题求解不适定性,提高了算法损伤识别的鲁棒性。数值仿真和框架结构振动实验结果均表明,对于线性或非线性结构,本文方法均能有效抑制噪声干扰,准确识别不同损伤工况下结构损伤的位置与程度;在试验研究中,结构损伤参数识别结果的相对误差小于1.5%。     

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。     

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.     

Optimization of regularization parameter of inversion in particle sizing using light extinction method

In particle sizing by light extinction method, the regularization parameter plays an important role in applying regularization to find the solution to ill-posed inverse problems. We combine the generalized cross-validation （GCV） and L-curve criteria with the Twomey-NNLS algorithm in parameter optimization. Numerical simulation and experimental validation show that the resistance of the newly developed algorithms to measurement errors can be improved leading to stable inversion results for unimodal particle size distribution.     

Erratum to: Parameter identification of electronic throttle using a hybrid optimization algorithm

Aiming at the problems in parameter identification of an electronic throttle, this paper proposes a novel hybrid optimization algorithm to search the optimal parameter values of the plant. The parameter identification of an electronic throttle is considered as an optimization process with an objective function minimizing the errors between the measurement and identification, and the optimal parameter values of the plant are searched by using a hybrid optimization algorithm. The proposed hybrid optimization algorithm, effective combination of parallel chaos optimization algorithm (PCOA) and simplex search method, preserves both the global optimization capability of PCOA and the accurate search ability of simplex search method. Simulation and experiment results have shown the good performance of the proposed approach.     

A new design method for adaptive IIR system identification using hybrid particle swarm optimization and gravitational search algorithm

Design of adaptive infinite impulse response (IIR) filter is the process of utilizing adaptive algorithm to iteratively determine the filter parameters to obtain an optimal model for the unknown plant based on minimizing the error cost function. However, the error cost surface of IIR filter is generally nonlinear, non-differentiable and multimodal. Hence, an efficient global optimization technique is required to minimize the error cost objective. A novel hybrid particle swarm optimization and gravitational search algorithm (HPSO–GSA) is proposed in this paper for IIR filter design. The proposed HPSO–GSA updates particle positions through obeying the influence of gravity acceleration in GSA and receiving direction of cognitive memory and social sharing information from PSO by means of coevolutionary strategy. The effect of key parameters on the performance of the proposed algorithm is firstly studied, and the proper parameters in HPSO–GSA are established using five benchmark plants along with the same-order model. The simulation studies have been performed for the performance comparison of eight algorithms such as PSO, GSA, QPSO, DPSO, FO-DPSO, GAPSO, PSOGSA and the proposed HPSO–GSA for unknown IIR system identification with the same-order and reduced-order filters. Simulation results show that the proposed algorithm has advantages over PSO, GSA and other PSO-based variants in terms of the convergence speed and the MSE levels.     

Parameter identification of electronic throttle using a hybrid optimization algorithm

Aiming at the problems in parameter identification of an electronic throttle, this paper proposes a novel hybrid optimization algorithm to search the optimal parameter values of the plant. The parameter identification of an electronic throttle is considered as an optimization process with an objective function minimizing the errors between the measurement and identification, and the optimal parameter values of the plant are searched by using a hybrid optimization algorithm. The proposed hybrid optimization algorithm, effective combination of parallel chaos optimization algorithm (PCOA) and simplex search method, preserves both the global optimization capability of PCOA and the accurate search ability of simplex search method. Simulation and experiment results have shown the good performance of the proposed approach.     

Hybrid approach for dynamic model identification of an electro-hydraulic parallel platform

In this paper, a hybrid optimization algorithm is proposed to identify the dynamic parameters of a 6-DOF electro-hydraulic parallel platform. The dynamic model of a parallel platform with arbitrary geometry, inertia distribution and frictions is obtained based on a structured Boltzmann–Hamel–d’Alembert formulation, and then the estimation equations are explicitly expressed in terms of a linear form with respect to the identified inertial and the friction coefficients in accordance with a linear friction model. However, when nonlinear friction models are considered, the parameter identification of the electro-hydraulic parallel platform is considered as an optimization process with an objective function minimizing the errors between the measurement and identification, and then an effective combination of the particle swarm optimization (PSO) method and the local quasi-Newton method is proposed to solve the identification problem. Experimental identification processes are carried out for the identified parameters, and the identified models are compared by the predicted forces between the LS method and the optimization technique as well as between the linear and nonlinear friction models.     

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.     

A novel APSO-aided maximum likelihood identification method for Hammerstein systems

Identification of Hammerstein nonlinear models has received much attention due to its ability to describe a wide variety of nonlinear systems. In this paper the maximum likelihood estimator which was originally derived for linear systems is extended to work for Hammerstein nonlinear systems in colored-noise environment. The maximum likelihood estimate is known to be statistically efficient, but can lead to complex nonlinear multidimensional optimization problem; traditional methods solve this problem at the computational cost of evaluating second derivatives. To overcome these shortcomings, a particle swarm optimization (PSO) aided maximum likelihood identification algorithm (Maximum Likelihood-Particle Swarm Optimization, ML-PSO) is first proposed to integrate PSO’s simplicity in implementation and computation, and its ability to quickly converge to a reasonably good solution. Furthermore, a novel adaptive strategy using the evolution state estimation technique is proposed to improve PSO’s performance (maximum likelihood-adaptive particle swarm optimization, ML-APSO). A simulation example shows that ML-APSO method outperforms ML-PSO and traditional recursive least square method in various noise conditions, and thus proves the effectiveness of the proposed identification scheme.     

求解非线性方程组的混沌粒子群算法及应用

针对非线性方程组的求解在工程上具有广泛的实际意义,经典的数值算法如牛顿法存在其收敛性依赖于初值而实际计算中初值难确定的问题,提出以混沌粒子群算法求解非线性方程。它通过将混沌搜索机制有机地引入粒子群算法,使每个粒子从混沌搜索机制与粒子群算法搜索机制中获得适当的搜索方向,以混沌变量的遍历性增强粒子的搜索性能与更全面地应用目标函数的信息,并反映到逐代更新的个体极值和群体极值中,可更有效地调整粒子的移向并最终获得最优解。测试结果表明这一尝试的有效性。最后将所提的方法用于建立复合材料结构的疲劳寿命与应力、温度、湿度的关系模型。     

Direct Hydraulic Parameter and Function Estimation for Diverse Soil Types Under Infiltration and Evaporation

A new computationally efficient direct method is applied to estimating unsaturated hydraulic properties during steady-state infiltration and evaporation at soil surface. For different soil types with homogeneous and layered heterogeneity, soil hydraulic parameters and unsaturated conductivities are estimated. Unlike the traditional indirect inversion method, the direct method does not require forward simulations to assess the measurement-to-model fit; thus, the knowledge of model boundary conditions (BC) is not required. Instead, the method employs a set of local approximate solutions to impose continuity of pressure head and soil water fluxes throughout the inversion domain, while measurements act to condition these solutions. Given sufficient measurements, it yields a well-posed system of nonlinear equations that can be solved with optimization in a single step and is thus computationally efficient. For both Gardner’s and van Genuchten’s soil water models, unsaturated hydraulic conductivities and pressure heads (including the unknown BC) can be accurately recovered. When increasing measurement errors are imposed, inversion becomes less accurate, but the solution is stable, i.e., estimation errors remain bounded. Moreover, when the unsaturated conductivity model is known, inversion can recover its parameters; if it is unknown, inversion can recover a nonparametric, piecewise continuous function to which soil parameters can be obtained via fitting. Overall, inversion accuracy of the direct method is influenced by (1) measurement density and errors; (2) rate of infiltration or evaporation; (3) variation of the unsaturated conductivity; (4) flow direction; (5) the number of soil layers.     

材料热传导系数随温度变化函数的反演方法

材料热传导系数的反演是一类典型的热传导逆问题。针对材料热传导系数随温度变化的情况,本文将材料的热传导系数值按温度区间分段离散,建立了通过材料边界点的温度测量来反演各温度区间热传导系数值的遗传算法和伴随方程法。通过典型算例分析和考虑测量噪声、系统噪声的反演计算结果分析表明：所建立的两种反演算法都是可行有效的,受测量随机噪...