Robot stereo vision calibration method with genetic algorithm and particle swarm optimization

Accurate stereo vision calibration is a preliminary step towards high-precision visual posi- tioning of robot. Combining with the characteristics of genetic algorithm （GA） and particle swarm optimization （PSO）, a three-stage calibration method based on hybrid intelligent optimization is pro- posed for nonlinear camera models in this paper. The motivation is to improve the accuracy of the calibration process. In this approach, the stereo vision calibration is considered as an optimization problem that can be solved by the GA and PSO. The initial linear values can be obtained in the frost stage. Then in the second stage, two cameras＇ parameters are optimized separately. Finally, the in- tegrated optimized calibration of two models is obtained in the third stage. Direct linear transforma- tion （DLT）, GA and PSO are individually used in three stages. It is shown that the results of every stage can correctly find near-optimal solution and it can be used to initialize the next stage. Simula- tion analysis and actual experimental results indicate that this calibration method works more accu- rate and robust in noisy environment compared with traditional calibration methods. The proposed method can fulfill the requirements of robot sophisticated visual operation.

Structural Parameter Optimization of Multilayer Conductors in HTS Cable

In this paper, the design optimization of the structural parameters of multilayer conductors in high temperature superconducting （HTS） cable is reviewed. Various optimization methods, such as the particle swarm optimization （PSO）, the genetic algorithm （GA）, and a robust optimization method based on design for six sigma （DFSS）, have been applied to realize uniform current distribution among the multilayer HTS conductors. The continuous and discrete variables, such as the winding angle, radius, and winding direction of each layer, are chosen as the design parameters. Under the constraints of the mechanical properties and critical current, PSO is proven to be a more powerful tool than GA for structural parameter optimization, and DFSS can not only achieve a uniform current distribution, but also improve significantly the reliability and robustness of the HTS cable quality.

Comparison of Parallel Genetic Algorithm and Particle Swarm Optimization for Parameter Calibration in Hydrological Simulation

Parameter calibration is an important part of hydrological simulation and affects the final simulation results.In this paper,we introduce heuristic optimization algorithms,genetic algorithm(GA)to cope with the complexity of the parameter calibration problem,and use particle swarm optimization algorithm(PsO)as a comparison.For large-scale hydrological simulations,we use a multilevel parallel parameter calibration framework to make full use of processor resources,and accelerate the process of solving high-dimensional parameter calibration.Further,we test and apply the experiments on domestic supercomputers.The results of parameter calibration with GA and PSO can basically reach the ideal value of 0.65 and above,with PSO achieving a speedup of 58.52 on TianHe-2 supercomputer.The experimental results indicate that using a parallel implementation on multicore CPUs makes high-dimensional parameter calibration in large-scale hydrological simulation possible.Moreover,our comparison of the two algorithms shows that the GA obtains better calibration results,and the PSO has a more pronounced acceleration effect.

Parameter Identification Based on a Modified PSO Applied to Suspension System

This paper presents a novel modified particle swarm optimization algorithm (MPSO) for both offline and online parametric identification of dynamic models. The MPSO is applied for identifying a suspension system introduced by a quarter-car model. A novel mutation mechanism is employed in MPSO to enhance global search ability and increase convergence speed of basic PSO (BPSO) algorithm. MPSO optimization is used to find the optimum values of parameters by minimizing the sum of squares error. The performance of the MPSO is compared with other optimization methods including BPSO and Genetic Algorithm (GA) in offline parameter identification. The simulating results show that this algorithm not only has advantage of convergence property over BPSO and GA, but also can avoid the premature convergence problem effectively. The MPSO algorithm is also improved to detect and determine the variation of parameters. This novel algorithm is successfully applied for online parameter identification of suspension system.

Genetic programming-based chaotic time series modeling

This paper proposes a Genetic Programming-Based Modeling (GPM) algorithm on chaotic time series. GP is used here to search for appropriate model structures in function space, and the Particle Swarm Optimization (PSO) algorithm is used for Nonlinear Parameter Estimation (NPE) of dynamic model structures. In addition, GPM integrates the results of Nonlinear Time Series Analysis (NTSA) to adjust the parameters and takes them as the criteria of established models. Experiments showed the effectiveness of such improvements on chaotic time series modeling.

GA and PSO culled hybrid technique for economic dispatch problem with prohibited operating zones

This paper presents an efficient and reliable genetic algorithm (GA) based particle swarm optimization (PSO) tech- nique (hybrid GAPSO) for solving the economic dispatch (ED) problem in power systems. The non-linear characteristics of the generators, such as prohibited operating zones, ramp rate limits and non-smooth cost functions of the practical generator operation are considered. The proposed hybrid algorithm is demonstrated for three different systems and the performance is compared with the GA and PSO in terms of solution quality and computation efficiency. Comparison of results proved that the proposed algo- rithm can obtain higher quality solutions efficiently in ED problems. A comprehensive software package is developed using MATLAB.

A Hybrid Algorithm Based on PSO and GA for Feature Selection

One of the main problems of machine learning and data mining is to develop a basic model with a few features,to reduce the algorithms involved in classification’s computational complexity.In this paper,the collection of features has an essential importance in the classification process to be able minimize computational time,which decreases data size and increases the precision and effectiveness of specific machine learning activities.Due to its superiority to conventional optimization methods,several metaheuristics have been used to resolve FS issues.This is why hybrid metaheuristics help increase the search and convergence rate of the critical algorithms.A modern hybrid selection algorithm combining the two algorithms;the genetic algorithm(GA)and the Particle Swarm Optimization(PSO)to enhance search capabilities is developed in this paper.The efficacy of our proposed method is illustrated in a series of simulation phases,using the UCI learning array as a benchmark dataset.

混合热工和水力特性的风机盘管系统建模

针对风机盘管系统建模研究中大多数未考虑水力学特性对其的影响,本文提出混合热工和水力特性的风机盘管系统建模方法,以满足实际工程中对于风机盘管系统控制的数学模型需要.在水力学模型中考虑风机盘管电动水阀,基于基尔霍夫定律建立水力平衡方程,通过遗传–粒子群相结合的优化算法(GA-PSO)求解各个盘管的水流量及对应的阀门开度;以水流量为输入,考虑风机盘管风量,采用非线性最小二乘法辨识求解其总传热系数,建立基于牛顿冷却定律的风机盘管热力学模型.实验结果表明:混合热工和水力特性的建模方法能够更好的反应风机盘管的物理过程;GA-PSO算法具有很好的稳定性和收敛性,能够更准确的求解水流量及对应的阀门开度,与实际运行数据的相对误差不超过1%;热力学模型在各种实际工况下的模型误差都低于4%,能够精确反映风机盘管的传热过程.

基于改进粒子群算法的卫星星座优化设计

面向区域覆盖的卫星星座设计是一个多约束的优化问题,提出一种基于改进粒子群(Particle Swarm Optimization,PSO)算法的中轨(Medium Earth Orbit,MEO)卫星星座优化设计方法。以最大化卫星星座对中国区域的平均覆盖率为目标,基于3+4P星座构型进行卫星星座优化设计,并采用改进PSO算法对卫星的轨道参数进行优化。通过仿真软件和卫星仿真工具包(Satellite Tool Kit,STK)互联进行算法验证,并将改进PSO算法、标准PSO算法和遗传算法(Genetic Algorithm,GA)的优化结果进行对比。仿真结果表明,改进PSO算法优化后的卫星星座对中国区域的覆盖率均值为95.34%,分别比标准PSO算法和GA算法高0.68%和9.55%,同时具有更高的平均覆盖重数和总覆盖时长。因此,基于改进PSO算法的卫星星座优化设计方法可以实现较好的覆盖性能。

天牛须算法在优化磁致伸缩换能器中的应用

磁致伸缩换能器换能效率不高是锚杆无损检测准确度较低的重要原因,确定合适的线圈结构参数对提高换能效率至关重要。基于Comsol多物理场有限元仿真模拟软件建立锚杆无损检测模型,首先,确定不同线圈匝数和不同提离距离对换能效率的影响规律;其次,将天牛须算法和粒子群遗传算法应用于线圈参数的本体优化问题,提出以提高换能器换能效率为优化目标、以线圈匝数和线圈提离距离为自变量的单目标优化设计模型,筛选出最优的线圈结构参数;最后,搭建锚杆无损检测实验平台,将自变量取值范围内的数值分别进行实验验证。研究结果表明:增加线圈的匝数、缩短线圈提离距离可以提高磁致伸缩换能器的换能效率;天牛须算法和粒子群遗传算法优化参数相同且符合仿真得到的影响规律,相比于粒子群遗传算法,天牛须算法原理简单、参数少、运算量少,在处理低维优化问题时具有更大的优越性;实验得到的线圈参数取值与算法优化结果一样,验证了天牛须算法用于锚杆换能器参数优化可靠且快速。

基于GA-PSO算法的冻土本构模型参数识别

遗传算法(GA)与粒子群算法(PSO)分别具有缺乏目标导向性和易陷入局部最优的缺点,但同时分别具有全局搜索能力强与能有效传递优势信息的优点。本文以GA计算步结合精英保留策略作为PSO计算步的优势信息,避免PSO算法陷入局部最优,以PSO计算步结合非精英优化策略作为GA计算步的导向信息,克服GA算法缺乏目标导向的问题,建立了GA-PSO新算法。其具体过程为,通过采用GA计算步对解空间进行全局搜索并对精英个体进行保留,进一步,将适应度较差的个体利用PSO计算步进行优化。基于多峰函数的验证结果表明,GA-PSO算法在解空间中具有更强的全局搜索能力,同时具有更快的收敛速度。将GA-PSO算法应用到冻土非正交弹塑性本构模型的参数识别中,通过模型的参数识别以及模型预测结果对比与验证,结果表明GA-PSO算法能够有效识别冻土非正交弹塑性本构模型的参数,提升了模型的预测效果。

基于相量测量单元优化配置的配电网谐波状态估计研究

随着大量电力电子设备的接入,配电网谐波问题愈发严重。谐波状态估计的准确性直接影响到后续的谐波治理效果。相量测量单元(phasor measurement unit,PMU)可以实时测量节点电压与支路电流,可借助其实现谐波状态估计。然而目前PMU价格较高,如何进行合理的优化配置保证全网谐波状态可观,同时提高谐波状态估计的准确性,是亟待解决的问题。首先构建以PMU经济配置和谐波状态估计精度最高为目标的PMU优化配置模型,并提出一种改进二进制粒子群-遗传混合算法用于求解。随后在实时仿真器中搭建IEEE14节点模型,选用均值插补法以及Vondrak滤波法进行数据处理并分析了优化所得多种PMU配置场景对谐波状态估计的影响。结果表明:所提算法从减少投资成本及降低谐波状态估计误差角度考虑,能够给出合理的PMU配置方案,有助于支撑工程决策。

基于粒子群算法的农用轮胎柔性环模型参数辨识方法

轮胎柔性环模型能准确表达轮胎变形,但模型的刚度参数无法直接测定,因此模型刚度参数的辨识成为建模过程中的关键。本文基于轮胎柔性环模型运动学方程,分析农用轮胎固有频率与刚度参数之间的关系,提出基于粒子群算法的柔性环模型刚度参数辨识方法。通过轮胎模态试验获取轮胎固有频率,采用粒子群算法对柔性环模型刚度参数进行辨识。将固有频率的试验值与预测值的平均误差作为评价指标,对比粒子群算法与传统算法及遗传算法辨识结果,结果表明粒子群算法的参数辨识结果精度较高,平均绝对误差为1.67 Hz,平均相对误差为1.66%,相较于遗传算法,平均相对误差降低16.16%,运算时间减少93.19%。通过接地印痕试验获取农用轮胎接地角度,结合辨识所得刚度参数,估算轮胎所受到的垂向力,对比垂向力的试验值与预测值,结果表明粒子群算法的参数辨识结果精度较高,垂向载荷估算平均相对误差为1.97%,相对于遗传算法,平均相对误差降低12.05%。

功率分流式混合动力汽车整车参数匹配与优化研究

针对功率分流式混合动力汽车,首先根据整车设计参数及动力性能目标要求,分别对电机、动力电池组、发动机、主减速器和行星排等传动系统部件进行参数匹配,并运用AVL-Cruise软件建立目标车辆整车模型;其次在满足动力性能目标的前提下,运用AVL-Cruise和Isight软件搭建联合仿真模型,分别采用粒子群优化算法和多岛遗传算法,将行星排特征参数和主减速器传动比作为优化参数,百公里油耗和电耗加权之和作为优化目标进行优化研究,并对比分析2种优化算法的计算结果来获得兼顾动力性能和经济性的最优结构参数,为研究功率分流式混合动力汽车传动系统提供理论依据。

混合遗传算法下电动汽车充电桩容量测试方法

由于接入电动汽车充电桩的负荷增多,导致充电线路过载,增加能量损耗,使得电网安全风险增高。为解决上述问题,提出基于混合遗传的电动汽车充电桩容量测试方法。分析车辆运行机制、电池特征、充电方式等因素对充电桩容量的影响,计算车辆充电定点需求与路径需求,设置合理的约束条件;将充电桩容量看作一个满足多个约束条件的可行空间,在满足发电容量、功率平衡、潮流平衡、电压稳定等约束条件下,构建容量测试模型;针对遗传算法收敛速度慢的缺陷,使用粒子群算法对其改进,通过个体寻优过程求解模型,获得测试值。实验结果表明,所提方法在峰荷、腰荷和基荷场景下的测量结果与真实值基本一致,且测量耗时低于6s,算法收敛速度快。

混合驱动的粒子群算法

粒子群优化(particle swarm optimization,PSO)算法是一种在机器人运动规划、信号处理等领域有广泛应用的优化算法。然而该算法易陷入局部最优解,从而导致早熟问题。出现早熟问题的原因之一是粒子群仅依靠适应度值选择学习范例。为了克服上述问题,提出了一种基于适应度值、改进率和新颖性混合驱动的PSO算法(particle swarm optimization algorithm based on hybrid driven by fitness values,improvement rate,and novelty,FINPSO)。在该算法中,引入的新指标和遗传算法会平衡种群的探索与开发,降低粒子群早熟的可能性。适应度值、改进率和新颖性会作为粒子的评价指标。各指标独立地选择学习范例并保存到不同的档案中。粒子每一次速度更新都要确定各个指标的权重,并从每个档案中选择一个范例学习。该算法采用了遗传算法进行粒子间的信息交流。遗传算法中的交叉互换和突变会给种群带来更多的随机性,提升种群的全局搜索能力。以八个PSO算法变体作为对比算法,两个CEC测试套件作为基准函数进行实验。实验结果表明,FINPSO算法优于已有的PSO算法变体达到最先进水平。

基于粒子群-遗传混合算法的深沟球轴承优化设计

为了提高深沟球轴承的服役性能,提出一种基于粒子群-遗传混合算法的优化设计方法。其以额定动载荷和额定静载荷为目标函数,以滚动体直径、节圆直径、滚动体数目和内外圈滚道沟曲率半径系数为设计变量,基于粒子群算法,引入罚函数和遗传交叉、变异操作,解决带约束优化问题求解和局部最优问题。并以6206型轴承为算例,对优化后的轴承进行应力分析和敏感度分析。结果表明,所提出算法的收敛性能较好、优化能力较强、运算速度较快,优化后的深沟球轴承接触应力下降了31.7%,从而验证了所提出方法的有效性。

群体智能算法在路面参数反分析中的适用性及优选策略

随着群体智能算法在路面参数反演中的成功应用,复杂多元非线性优化难题得以解决,但算法的选择仍然是路面参数反分析问题中亟待解决的难题。针对路面参数反分析中模型复杂、反演参数众多、绝大多数运算时间消耗在正算程序上等问题,选择8种常见的群体智能算法,开展限定正算调用次数下算法性能相关研究,并以考虑材料横观各向同性以及层间接触状态的路面结构参数反演问题为例,对群体智能算法进行实际测试。结果表明:不同算法各具特点,其中,粒子群算法、遗传算法、头脑风暴算法、人工蜂群算法以及烟花算法在多峰问题上具有较好的适用性;萤火虫算法在解决最优解附近存在平缓区域的问题时具有较快的收敛速度;对于遗传算法,实数编码方式后期收敛速度较二进制编码方式有所提高,但对于多峰问题的搜索能力有所下降;鱼群算法、混合蛙跳算法仅有在较大正算调用次数下才有较好的寻优能力。对于路面参数反演问题,从弯沉曲线匹配上看,粒子群算法、遗传算法、头脑风暴算法以及萤火虫算法均有较好的反演结果;而从相关系数上看,头脑风暴算法具有最佳反演结果。

复杂载荷下的核管路支架智能布置

当前,核电管路支架布置通过人工试算反复迭代来实现,存在劳动强度大、设计周期长、成本高等缺点。借助管路有限元力学分析,提出一种基于粒子群-遗传混合算法的三维空间核电管路支架智能布置方法。布置示例表明,所提方法可在管路的三维全局空间内自动布置不同功能的多个支架,在满足《压水堆核岛机械设备设计和建造规则》设计规范的同时减小了工作量。

基于粒子群遗传算法的纱线生产过程参数反演

针对传统纱线质量的正演、反演模型中存在收敛速度慢、精度低等问题,以及标准粒子群算法存在陷入局部极值的缺陷,提出一种粒子群遗传混合算法。使用该算法优化BP神经网络的权值和阈值并建立纱线条干正演模型。在此基础上,以纱线条干CV值为对象构建了粒子群遗传算法反演模型;使用历史生产数据对生产过程参数进行反演。结果表明:各生产过程参数反演结果的平均相对误差均低于4%。认为:该反演方法具有较高的可行性与准确性。