一种改进的CVP方法及其在动态优化中的应用

针对控制向量参数化方法敏感度方程求解耗时长、时间节点数难确定等问题,提出一种改进的控制向量参数化方法．首先利用分段常数对系统敏感度方程进行近似处理,有效地得到了敏感度方程的近似解析解,避免了对高维敏感度方程数值积分的计算负担;然后根据目标函数关于控制参数的敏感度来选择需要细化的控制参数,得到满足优化精度要求的最优时间节点数．针对非线性ＣＳＴＲ 的仿真研究验证了所提出算法的可行性和有效性．

     

一种改进的SMO及其在人脸识别中的应用

SMO算法是一种有效的SVM训练算法,但由于参与训练的数据大部分为非支持向量,仍然存在进一步优化的可能性。针对SMO算法的这个不足,提出了一种改进的SMO算法,并将该算法应用到人脸识别中。试验结果表明了算法的有效性。     

一种动态优化的无线信道速率控制方法

速率控制是无线视频传输过程中重要的控制方法,在无线网络中采用了基于TCP友好速率控制TFRC(TCP-Friendly Rate Control)的速率控制方法,提出了基于动态优化的无线视频传输多用户TFRC的速率控制方法。该方法利用了自适应编码调制特点,根据用户的信道状况,动态优化物理层发送速率;经过理论分析和仿真证明可以在总的链路速率不增加的情况下,动态地根据各个用户的信道状况变化情况,公平地分配和调整各个用户带宽,从而最大效率地使用现有链路及提高整体的用户满意度。     

一种非线性优化控制方法及其在鱼雷控制中的应用

提出了一种带优化修正函数的非线性ＰＩＤ控制器设计方法,通过对控制器参数离线寻优,并对修正函数在线优化调整,可用它设计出性能优良且易于工程实现的控制器,然后用地为某型鱼雷非线性系统设计了弹道深义控制器并在各种人水条件下进行了仿真研究,仿真表明,所设计的鱼雷非线笥弹道深度控制系统具有良好的动、静态特性,且对鱼雷人水条件的变化具有较强的适应性。     

一种改进的综合生产计划动态规划优化方法

提出了一种改进的综合生产计划动态规划优化方法。以1999年甘应爱主编的《运筹学》第227~230页给出的一类综合生产计划问题为研究对象,深入分析了原综合生产计划问题、数学优化模型、动态规划求解过程、计算方法存在的不足并提出了相应的改进措施。通过案例分析验证了所提方法的有效性。     

一种基于数据的核参数优化方法

SVM是一种基于核的学习方法,核及相关参数的选择对其性能有非常重要的影响,提出了一种数据依赖的最优核参数估计方法,通过角度切割样本集求解训练样本的近似凸包,以确定最优的核参数。实验结果表明,无论数据是否稠密,分布是否均匀,算法都可适用,该方法有较高的可行性与有效性。     

一种改进的药代动力学参数优化方法及其应用

基于药代动力学参数优化方法PKAIN人工免疫网络算法,提出了迭代分组并发单纯形算子,并实现了线性网络抑制函数以简化人工免疫网络的参数设置。为了加快算法的搜索速度和搜索精度,提出了新型的人工免疫网络单纯形混合算法（PKAIN_spx）,用人工免疫网络实现粗粒度全局搜索,随后用单纯形进行精确搜索。仿真实验对改进的PKAIN算法（PKAIN_in）、人工免疫网络单纯形混合算法（PKAIN_spx）以及PKAIN算法进行了比较分析,结果表明PKAIN_spx算法在药代动力学参数优化中取得良好的实验效果。     

一种改进细菌觅食优化算法及其在软测量建模中的应用

针对软测量建模中模型参数的优化需求,在分析细菌觅食优化算法(BFOA)和粒子群优化(PSO)算法的基础上,将二者有机结合,提出了一种新型细菌觅食粒子群混合优化算法(BSOA)。该算法将PSO粒子移动的思想引入BFOA,有效解决了BFOA趋向性操作中细菌位置更新的盲目性。将其分别用于典型函数的寻优与成品油研究法辛烷值最小二乘支持向量机(LSSVM)模型参数的优化,仿真结果表明:该方法有效增强了算法的全局寻优能力与收敛速度,并在一定程度上改善了模型的预测精度与泛化能力。     

Efficient shape parameterization method for multidisciplinary global optimization and application to integrated ship hull shape optimization workflow

Multidisciplinary global shape optimization requires a geometric parameterization method that keeps the shape generality while lowering the number of free variables. This paper presents a reduced parameter set parameterization method based on integral B-spline surface capable of both shape and topology variations and suitable for global multidisciplinary optimization. The objective of the paper is to illustrate the advantages of the proposed method in comparison to standard parameterization and to prove that the proposed method can be used in an integrated multidisciplinary workflow. Non-linear fitting is used to test the proposed parameterization performance before the actual optimization. The parameterization method can in this way be tested and pre-selected based on previously existing geometries. Fitting tests were conducted on three shapes with dissimilar geometrical features, and great improvement in shape generality while reducing the number of shape parameters was achieved. The best results are obtained for a small number (up to 50) of optimization variables, where a classical applying of parameterization method requires about two times as many optimization variables to obtain the same fitting capacity.The proposed shape parameterization method was tested in a multidisciplinary ship hull optimization workflow to confirm that it can actually be used in multiobjective optimization problems. The workflow integrates shape parameterization with hydrodynamic, structural and geometry analysis tools. In comparison to classical local and global optimization methods, the evolutionary algorithm allows for fully autonomous design with an ability to generate a wide Pareto front without a need for an initial solution.     

Design parameterization and tool integration for CAD-based mechanism optimization

This paper presents an open and integrated tool environment that enables engineers to effectively search, in a CAD solid model form, for a mechanism design with optimal kinematic and dynamic performance. In order to demonstrate the feasibility of such an environment, design parameterization that supports capturing design intents in product solid models must be available, and advanced modeling, simulation, and optimization technologies implemented in engineering software tools must be incorporated. In this paper, the design parameterization capabilities developed previously have been applied to support design optimization of engineering products, including a High Mobility Multi-purpose Wheeled Vehicle (HMMWV). In the proposed environment, Pro/ENGINEER and SolidWorks are supported for product model representation, DADS (Dynamic Analysis and Design System) is employed for dynamic simulation of mechanical systems including ground vehicles, and DOT (Design Optimization Tool) is included for a batch mode design optimization. In addition to the commercial tools, a number of software modules have been implemented to support the integration; e.g., interface modules for data retrieval, and model update modules for updating CAD and simulation models in accordance with design changes. Note that in this research, the overall finite difference method has been adopted to support design sensitivity analysis.     

柴油机活塞碗参数化建模方法及在优化设计中的应用

为解决活塞碗排放优化过程中的参数驱动问题,提出一种柴油机活塞碗的参数化建模方法。该方法能够确保在活塞碗轮廓线变形过程中燃烧室的控制容积不变,以满足恒定压缩比的要求。采用CAESES建立活塞碗的参数化模型,在CONVERGE中对活塞碗内的燃烧进行分析,使用CAESES的遗传算法驱动优化流程,通过案例验证该方法的有效性。结果表明：遗传算法对活塞碗进行4代优化后得到的算例最佳,NOx浓度降低66%,Soot浓度降低78%。     

增强型野马优化算法及其工程应用

针对野马优化算法易陷入局部最优、收敛速度慢等缺点,提出增强型野马优化算法。首先,在种群初始化阶段,采用Sinusoidal映射,增加种群的多样性;其次,在阶段更新过程中,设计出非线性收敛性更强的自适应权重,调节全局搜索和局部优化能力;然后,在更新领导者位置阶段加入扰动因子,平衡局部和全局探索能力;进一步,利用自适应t分布变异,对个体位置进行扰动,提高算法跳出局部最优的能力。通过在CEC2021测试竞赛进行测试优化比较,验证算法的有效性和稳健性,并利用Wilcoxon秩和检验和MAE排名,验证算法的有效性。最后将算法应用到工程难题问题中,验证了该算法在工程优化问题上的适用性与优越性。实验结果表明,与其他智能算法相比,增强型野马优化算法具有更强的寻优能力和更快的收敛速度。     

Restricted second order information for the solution of optimal control problems using control vector parameterization

A new scheme using a Truncated Newton algorithm with and exact Hessian-search direction vector product is presented for the solution of optimal control problems. The derivation of formulae for second order parametric sensitivity analysis of differential-algebraic equations is presented, following earlier published work [V.S. Vassiliadis, E. Balsa-Canto, J.R. Banga, Second order sensitivities of general dynamic systems with application to optimal control problems. Chem. Eng. Sci. 54 (17) (1999) 3851–3860]. An original result in this work is the derivation of Hessian matrix-vector product forms which are shown to have the same computational complexity as the evaluation of first order sensitivities. This result for optimal control Hessian-vector products using control vector parameterization is shown to be a very effective way to solve optimal control problems. It is also noted that this work introduces the use of suitable Truncated Newton solvers which can exploit the exact vector products in using conjugate gradient iterations to converge the Newton equations. Such a solver is the TN algorithm of Nash [(S.G. Nash-Newton type minimization via the Lanczos method. SIAM J. Num. Anal. 21, (1984) 770–778)]. Because no full Hessian update is necessary it is demonstrated that the resulting optimal control solver performs very well for a very large number of degrees of freedom, limited only by the necessity for many right-hand-side calculations in the first and second order sensitivity equations (the Hessian vector product). It is also demonstrated by several case studies that the scheme is capable of starting far from the solution and yet arrive there in almost invariant performance.     

Parameter selection of support vector regression based on hybrid optimization algorithm and its application

Choosing optimal parameters for support vector regression （SVR） is an important step in SVR. design, which strongly affects the pefformance of SVR. In this paper, based on the analysis of influence of SVR parameters on generalization error, a new approach with two steps is proposed for selecting SVR parameters, First the kernel function and SVM parameters are optimized roughly through genetic algorithm, then the kernel parameter is finely adjusted by local linear search, This approach has been successfully applied to the prediction model of the sulfur content in hot metal. The experiment results show that the proposed approach can yield better generalization performance of SVR than other methods,     

Parameter selection of support vector regression based on hybrid optimization algorithm and its application

Choosing optimal parameters for support vector regression (SVR) is an important step in SVR design, which strongly affects the performance of SVR. In this paper, based on the analysis of influence of SVR parameters on generalization error, a new approach with two steps is proposed for selecting SVR parameters . First the kernel function and SVM parameters are optimized roughly through genetic algorithm, then the kernel parameter is finely adjusted by local linear search. This approach has been successfully applied to the prediction model of the sulfur content in hot metal. The experiment results show that the proposed approach can yield better generalization performance of SVR than other methods.     

支持向量机和粒子群算法在结构优化中的应用研究*

针对实际复杂结构优化中计算量大的问题,提出将支持向量机代理模型和粒子群算法应用于工程优化设计。采用实验设计选取合适的样本,通过实验或数值仿真获得性能响应,利用支持向量机构建目标函数和约束的代理模型,重构原始的优化问题,采用粒子群优化算法对重构的优化模型进行寻优,从而得到最优解。以典型电子装备功分器的结构尺寸优化为例,采用拉丁方实验设计和高频电磁场仿真软件HFSS获取代理模型的训练样本,建立功分器模型的幅度比、相位差和驻波三个目标函数模型,并对该多目标优化问题进行寻优。结果表明该方法准确、高效,为结构优化设     

Online adaptive least squares support vector machine and its application in utility boiler combustion optimization systems

Boiler combustion optimization is a key measure to improve the energy efficiency and reduce pollutants emissions of power units. However, time-variability of boiler combustion systems and lack of adaptive regression models pose great challenges for the application of the boiler combustion optimization technique. A recent approach to address these issues is to use the least squares support vector machine (LS-SVM), a computationally attractive machine learning technique with rather legible training processes and topologic structures, to model boiler combustion systems. In this paper, we propose an adaptive algorithm for the LS-SVM model, namely adaptive least squares support vector machine (ALS-SVM), with the aim of developing an adaptive boiler combustion model. The fundamental mechanism of the proposed algorithm is firstly introduced, followed by a detailed discussion on key functional components of the algorithm, including online updating of model parameters. A case study using a time-varying nonlinear function is then provided for model validation purposes, where model results illustrate that adaptive LS-SVM models can fit variable characteristics accurately after being updated with the ALS-SVM method. Based on the introduction to the proposed algorithm and the case study, a discussion is then delivered on the potential of applying the proposed ALS-SVM method in a boiler combustion optimization system, and a real-life fossil fuel power plant is taken as an instance to demonstrate its feasibility. Results show that the proposed adaptive model with the ALS-SVM method is able to track the time-varying characteristics of a boiler combustion system.     

矿山变频矢量控制系统效率优化方案研究

针对矿山变频调速系统感应电动机采用恒定磁通控制,存在损耗严重、运行效率低的问题,提出了一种矿山变频矢量控制系统的效率优化控制方案。该方案在两相旋转d-q坐标系下,建立了考虑定、转子铜损和铁损的系统损耗模型,推导得到不同运行工况下电动机损耗与转子磁链之间的对应关系;为改善因弱磁运行而造成的系统动态响应过慢问题,通过优化转矩电流和磁链电流比重关系,获得电动机电磁转矩的快速响应和转速跟踪。基于DSP+FPGA控制系统搭建22kW三电平感应电动机调速系统实验平台,实验结果表明,该优化方案可以降低感应电动机矢量控制系统运行损耗,提高系统运行效率,并保证系统的稳态性能和动态响应性能。