基于分群粒子群算法的平面度误差评定研究

基于分群粒子群算法对平面度误差判定进行了研究。首先建立平面度误差评定数学模型,对平面度误差最小求解转化成对目标函数的非线性最优化问题;接着改进粒子群算法把粒子群一分为二,在不增加粒子个数和粒子维度的情况下,两个粒子群分别用来全局搜索和局部搜索,通过阈值判断早熟现象;最后给出了算法流程。实例验证结果表明:该算法具有较强的优化能力,对测试函数求解的最优解值数据波动性比较小,平面度的公差值为0.0073mm,相比LSM、DM、TPM、PSO、ABC算法公差值平均分别减少了0.0023mm,0.0025mm,0.0027mm,0.0002mm,0.0005mm,评定精度较高。     

基于改进麻雀搜索算法的平面度误差评定方法

针对在平面度误差最小区域评定过程中易出现陷入局部最优、收敛速度慢和精度低的问题，提出了一种基于改进麻雀搜索算法(ISSA)的平面度误差评定方法。首先，采用具有更好遍历性的Kent混沌映射代替传统的Logistic混沌映射生成初始化种群，以增强算法的全局搜索能力；然后，应用一种基于光学透镜成像原理的反向学习策略以避免算法无法跳出局部最优；选用经典测试函数验证了ISSA算法的有效性，相对于SSA能够取得更好的寻优效果；最后，应用该方法对平面度误差进行评定，并与引用的其它方法进行比较。实验结果表明：基于ISSA算法的平面度误差评估方法用时0.488 4 s能够解得最小包容平面，与应用SSA算法相比减少了0.370 5 s,其计算精度与应用最小二乘法、遗传算法和粒子群算法的平面度误差评定方法相比分别减小了18.032 5μm、2.332 5μm、6.132 5μm。基于ISSA算法的平面度误差评估方法在优化效率、求解质量、计算精度和稳定性上均有优势，可应用于三坐标测量机等形位误差测量仪器。     

平面度误差粒子群算法评定的不确定度评估

形状误差智能评定采用概率化随机搜索,评定结果存在分散性,针对此问题提出一种不确定度评估方法。以平面度误差粒子群算法评定为例,根据智能评定结果概率分布特性,采用β分布统示法拟合其概率分布,并对平面度误差进行区间估计,最后选取仿真平面测量数据进行不确定度评估实验。结果表明,样本个数NS=100、截取百分位数Qp=20时,估计区间能够有效包容平面度误差值,且平均宽度较小,验证基于β分布统示法的智能评定不确定度评估可行性。     

基于最小包容区域法的平面度误差的快速评定法——新测点分类法

在平面度误差评定的最小包容区域法中提出一个新的、快速的实施方法:通过将测点向某特殊平面投影并利用在投影面中直线度评定的信息确定原测点的平面度误差.同时证明了从距最小二乘拟合面最远的高(低)点开始的搜索路线可减少测点分类法搜索的次数.本文通过一个常用的算例验证了该算法的正确性,并用随机产生若干算例验证了该算法的有效性.该算法对225个测点的平面度误差评定平均耗费时间只有0.5s左右.     

粒子群优化算法及其在圆度误差评定中的应用

提出一种基于粒子群优化算法(PSO)的圆度误差评定方法。介绍了PSO算法的提出及其特点;具体阐述了PSO算法的基本原理和实现步骤;提出圆度误差评定这一非线性优化问题,给出其优化目标函数及PSO算法的适应度函数和编码方式;结合实例对算法参数进行了设置,通过实例运算对PSO进行了正确性和精确性验算。实例证明该方法能够很好地解决圆度误差评定问题,与遗传算法具有相当的计算精度,能够获得精度较高的结果。而PSO的突出优点是简单易于实现,计算速度快。     

基于动态改变权重粒子群算法的球度误差评定

为了实现对球形工件球度误差的精确评定,在4种球度误差评定数学模型的基础上,对文献提供的两组数据采用一种动态改变权重的粒子群算法(PSO)进行计算,这种算法在优化迭代过程中使惯性权重值随粒子的位置和目标函数的性质而更新。与基本PSO算法、最小二乘法、遗传算法和一种改进的PSO算法进行了比较。实验结果显示,相比其他方法,在最小包容区域法模型下使用动态改变权重粒子群算法得到的球度误差最小,第1组数据只需迭代30代左右,约50ms即可收敛,第2组数据收敛也很迅速,且多次实验显示其稳定性很高。因此,所提算法可精确快速地评价球度误差。     

具有快速收敛特性蜂群算法的球度误差评定

将蜂群算法应用到球度误差评定中,给出最小区域球度误差评定模型.根据球度误差评定的特点,改进了基本蜂群算法.首先从雇佣蜂中按概率引进一组蜂群实现最优搜索,加快算法的收敛速度;再按照概率随机选择部分侦察蜂在当前最优解邻域内搜索,提高算法跳出局部最优的能力.通过典型测试函数验证了该算法的可行性.比较改进蜂群算法与几种典型群智...     

平面度误差最小区域新算法--有序判别法

提出一种平面度误差最小区域新算法———有序判别法。该方法以最小区域准则为基础，直接以排序的高点和低点构成的初始评定平面进行最小包容区域的判定和搜索，最终求得平面度误差值。该方法易于理解和掌握，搜索判别有序。实例运算表明，首轮搜索成功率高、速度快。     

基于圆周割线改进型粒子群优化算法的叶片临界颤振辨识方法研究

针对风力机叶片在颤振风速下的临界颤振现象,创新性地结合几何圆周割线和传统粒子群优化算法,首次设计了一种圆周割线改进型粒子群优化算法,应用于叶片临界颤振系统的参数辨识。该方法利用圆周上移动点的割线距离来动态调节全局学习因子和局部学习因子,针对优化辨识提高全局搜索和局部搜索的动态平衡性,避免陷入局部最优,提高算法的整体寻优性能和优化效率。仿真试验中,将该方法与多种先进粒子群优化算法(如改进型粒子群优化(MPSO)算法、基于线性递减惯性权重的粒子群优化(LDIW-PSO)算法、基于动态学习因子的免疫粒子群优化(IPSODCLF)算法)的辨识结果相比较,结果表明该辨识方法在辨识精度、计算时间和鲁棒性方面均具有显著的优越性。     

混合教与学算法在空间直线度评定中的应用

在最小区域准则条件下,为了提高空间直线度的评定精度,将教与学算法运用于空间直线度的误差评定中。汲取混合蛙跳算法的种群分组策略、洗牌策略和局部更新策略等算法思想,并将其引入到教与学优化算法(TLBO)的班级初始化与教学阶段之中,从而设计了一种混合教与学算法(HTLBO),用以增加学生个体间的信息交互能力和局部搜索能力,进一步增强算法的寻优能力。最后,通过采用两组空间直线度误差算例对HTLBO算法进行实例验证,并将实验结果与其他常用算法计算结果进行了对比,结果表明:HTLBO算法在空间直线度误差评定过程中,搜索能力强,收敛速度快,能够对空间直线度进行较高精度的评定。     

基于粒子群算法的空间直线度误差评定

提出了一种满足最小区域法的空间直线度误差评价的新方法--粒子群算法。根据最小区域条件,建立了空间直线的数学模型以及优化目标函数。阐述了粒子群优化算法的原理和实现方法,然后根据粒子群算法优化求解。实例表明该方法对于空间直线度误差评定等非线性优化问题能得到最优解,可用于三坐标测量机等测量系统的空间直线度误差测量的数据处理。     

基于模态参数及BAS-PSO优化算法的软基水闸有限元模型参数修正方法

正确合理的有限元模型对于软基水闸结构健康监测及性能评估至关重要,但水闸有限元模型参数的不确定性使得建立的水闸有限元模型难以准确地反映水闸结构真实的动力学特性,该文结合模态参数和基于天牛须搜索算法的粒子群(BAS-PSO)优化算法,提出了一种软基水闸有限元模型参数修正方法。选择对水闸模态参数影响较大的弹性模量和密度作为待修正参数,建立反映软基水闸待修正参数和模态参数之间非线性关系的基于遗传算法的支持向量回归(GA-SVR)代理模型;提出基于GA-SVR代理模型计算模态参数与水闸振动模态参数之间相对偏差最小的目标函数,构建软基水闸有限元模型参数修正的最优化数学模型;提出一种BAS-PSO优化算法来求解最优化数学模型,克服了局部最优和收敛速度慢的问题。通过软基水闸物理模型实例表明,修正的有限元模型计算的模态参数与水闸识别模态参数在数值上比较吻合,该文方法合理可靠且具有良好的可行性,可为软基水闸有限元模型参数修正提供一条新思路。     

基于微分几何法和粒子群算法计算椭球面面轮廓度误差

基于DGA算法和误差评定的最小区域准则建立了椭球面面轮廓度误差评定的数学模型,该模型将点到椭球面的最小距离求解问题转化为一个理想椭球面球心坐标的搜索问题;采用PSO算法求取所建立数学模型的最优解。实例表明在不考虑仪器测量误差的前提下,所提出的椭球面面轮廓度误差求取方法的有效性。     

An improved hybrid self-inertia weight adaptive particle swarm optimization algorithm with local search

As an evolutionary computing technique, particle swarm optimization (PSO) has good global search ability, but the swarm can easily lose its diversity, leading to premature convergence. To solve this problem, an improved self-inertia weight adaptive particle swarm optimization algorithm with a gradient-based local search strategy (SIW-APSO-LS) is proposed. This new algorithm balances the exploration capabilities of the improved inertia weight adaptive particle swarm optimization and the exploitation of the gradient-based local search strategy. The self-inertia weight adaptive particle swarm optimization (SIW-APSO) is used to search the solution. The SIW-APSO is updated with an evolutionary process in such a way that each particle iteratively improves its velocities and positions. The gradient-based local search focuses on the exploitation ability because it performs an accurate search following SIW-APSO. Experimental results verified that the proposed algorithm performed well compared with other PSO variants on a suite of benchmark optimization functions.     

Effective Form Error Assessment Using Improved Particle Swarm Optimization

This paper presents an improved variant of particle swarm optimization (MPSO) algorithm for the form error evaluation, from a set of coordinate measurement data points. In classical particle swarm optimization (PSO), new solution is updated by the existing one without really comparing which one is better. This behaviour is considered to be caused by lack in exploitation ability in the search space. The proposed algorithm generates new swarm position and fitness solution employing an improved and modified search equation. In this step, the swarm searches in proximity of the best solution of previous iteration to improve the exploitation behaviour. The particle swarm employs greedy selection procedure to choose the best candidate solution. A non-linear minimum zone objective function is formulated mathematically for each form error and consequently optimized using proposed MPSO algorithm. Five benchmark functions are used to prove the efficiency of the proposed MPSO algorithm, by comparing the proposed algorithm with established PSO and genetic algorithm. Finally, the results of the proposed MPSO algorithm are compared with previous literature and with other nature inspired algorithms on the same problem. The results validate that proposed MPSO algorithm is more efficient and accurate as compared to other conventional methods and is well suited for effective form error evaluation using CMMs.     

Drilling path optimization by the particle swarm optimization algorithm with global convergence characteristics

Drilling path optimization is one of the key problems in holes-machining. This paper presents a new approach to solve the drilling path optimization problem belonging to discrete space, based on the particle swarm optimization (PSO) algorithm. Since the standard PSO algorithm is not guaranteed to be global convergent or local convergent, based on the mathematical model, the algorithm is improved by adopting the method to generate the stop evolution particle once again to obtain the ability of convergence on the global optimization solution. Also, the operators are proposed by establishing the Order Exchange Unit (OEU) and the Order Exchange List (OEL) to satisfy the need of integer coding in drilling path optimization. The experimentations indicate that the improved algorithm has the characteristics of easy realization, fast convergence speed, and better global convergence capability. Hence the new PSO can play a role in solving the problem of drilling path optimization.     

A surrogate-assisted particle swarm optimization algorithm based on efficient global optimization for expensive black-box problems

Evolutionary algorithms cannot effectively handle computationally expensive problems because of the unaffordable computational cost brought by a large number of fitness evaluations. Therefore, surrogates are widely used to assist evolutionary algorithms in solving these problems. This article proposes an improved surrogate-assisted particle swarm optimization (ISAPSO) algorithm, in which a hybrid particle swarm optimization (PSO) is combined with global and local surrogates. The global surrogate is not only used to predict fitness values for reducing computational burden but also regarded as a global searcher to speed up the global search process of PSO by using an efficient global optimization algorithm, while the local one is constructed for a local search in the neighbourhood of the current optimal solution by finding the predicted optimal solution of the local surrogate. Empirical studies on 10 widely used benchmark problems and a real-world structural design optimization problem of a driving axle show that the ISAPSO algorithm is effective and highly competitive.     

基于天牛须-粒子群优化算法的大型中央空调系统节能控制

大型公共建筑中央空调系统送风末端数量多,负荷需求变化大,常用的控制方法虽能满足末端需求,却存在能耗巨大的问题。为此本文构建了一个空调系统送风和冷冻水系统的优化控制模型,以系统能耗为优化目标,使用天牛须搜索-粒子群优化（BAS-PSO）混合算法求解该问题,提高系统节能率,改善了传统PSO的缺陷。同时将该模型用于上海市某公共建筑集中式空调系统的空气调节子系统进行优化控制,结果表明：BAS-PSO与原有控制方案——定送风温度控制相比,最大节能量达252.02 kW,节能率为20%,而现场测试显示,使用该优化控制能在负荷率为0.55时达到14.6%的节能率,节能153.15 kW,证明了该优化控制模型及优化算法有可靠的应用前景。