多学科协同优化算法的分析和改进

针对协同优化算法计算量大、优化结果多为局部最优解的问题,提出了一种改进的协同优化算法。首先,在系统级一致性等式约束中采用改进的松弛因子,使系统级优化的可行域是存在的,且可行域的范围逐步减小,以保证子学科间的一致性;其次,在子学科中,将目标函数分为一致性目标函数和子学科最优目标函数两个部分,以不同的权重相加作为子学科的目标函数,既考虑了一致性,又兼顾了子学科独立性。最后,以各子学科级独立优化结果作为初始点进行优化。采用两个经典案例对改进算法进行验证,优化结果表明,改进的算法具有更好收敛速度和可行性。     

一种具有全局快速寻优的多学科协同优化方法

针对协同优化算法迭代次数多、易收敛于局部极值点问题,提出一种全局快速寻优的协同优化算法。在系统级一致性等式约束中采用改进后松弛因子,改进动态松弛因子使优化设计点快速收敛于极值点,静态松弛因子使优化设计点跳出局部极值点,确保系统目标函数得到全局最优解;子学科目标函数由一致性目标函数和子学科最优目标函数两个部分以不同权重相加组成,考虑一致性的同时,又兼顾子学科独立性。采用减速器优化案例对改进协同优化算法进行验证。仿真结果表明,改进后算法在保证最大约束值较小的前提下,可快速得到全局最优解且鲁棒性好。     

基于改进粒子群和模糊神经网络的雾霾分类研究

针对粒子群算法对初始种群敏感和易陷入局部最优解等问题,提出了佳点集理论结合多种群多策略协同进化算法改进的粒子群算法(IMPMSPSO).首先采用佳点集理论生成佳点作为初始种群,使种群分布更均匀而在一定程度上减弱其对位置的敏感性;然后利用协同进化算法,先将种群随机分成若干子种群,各子种群随机选择一种改进的进化策略并行计算,并进行最优位置的共享.经过测试,IMPMSPSO在计算精度和收敛速度上均优于其他算法.最后利用IMPMSPSO优化模糊神经网络初始权值和阈值构造分类预测模型,对雾霾污染等级进行分类预测.结果表明,与其他分类模型相比,该模型在各等级上的准确率均有提高.     

基于信息熵和混沌理论的遗传—蚁群协同优化算法

为了融合遗传算法和蚁群算法在解决组合优化问题方面的优势,提出一种基于信息熵和混沌理论的遗传.蚁群协同优化算法.利用信息熵产生初始群体,增加初始群体的多样性,并将混沌优化的遍历特性引入融合的遗传.蚁群算法,改进相关参数,实现参数的自适应控制以及遗传算法与蚁群算法混合优化策略的有机集成.通过仿真实例表明了混合智能算法在解决...     

一种改进的协同优化算法及其应用

针对协同优化方法收敛困难、优化效率低的问题,提出了一种改进的协同优化算法—ICO算法。通过引入自适应松弛因子将一致性等式约束转化为不等式约束,同时建立混合惩罚函数,将系统级约束优化问题转化为无约束优化问题,ICO算法较好地克服了传统协同优化算法难于收敛的缺点。标准算例实验结果表明,ICO算法能够有效提高优化的稳定性、可靠性和计算效率。优化结果显示了协同优化算法解决海洋供应船的设计优化问题的有效性,为解决更为复杂工程系统的设计优化问题奠定了基础。     

改进的多学科协同优化算法及其应用

多学科优化设计（MDO）是当前复杂系统工程设计中研究最活跃的领域.分析了标准多学科协同优化算法解决实际复杂MDO问题计算困难的原因,提出了基于试验设计的近似模型和智能优化的协同优化算法（NCO）.NCO算法继承了标准协同优化分布并行的思想,采用现代智能算法优化系统级减小优化陷入局部解的可能性,以试验设计为基础的高精度近似模型代替学科真实模型降低计算成本,平滑数值噪声.通过经典MDO测试算例与Alexandrov提出的改进松弛协同优化比较,优化结果表明,NCO能有效提高收敛速率,保证收敛结果的稳定性和可靠性,能更好地满足复杂系统工程优化需要.     

基于动态罚函数法的协同优化算法

为保证协同优化的系统级优化存在可行域,采用动态罚函数法,将学科间一致性约束条件下的系统级优化问题转化为无约束优化问题,提出了应用更为普遍的学科间不一致信息的定义形式,对各种定义形式进行了分析比较,并利用该值构造动态罚因子的表达式.从增强算法可靠性的角度,使用遗传算法来取代系统级优化问题中基于梯度的优化算法,同时减少了对优化函数的连续性要求,利用减速器典型算例对该方法进行了验证,结果表明该方法具有良好的优化性能.     

基于密度的K-means聚类中心选取的优化算法

针对传统的K-means算法对于初始聚类中心点和聚类数的敏感问题,提出了一种优化初始聚类中心选取的算法。该算法针对数据对象的分布密度以及计算最近两点的垂直中点方法来确定k个初始聚类中心,再结合均衡化函数对聚类个数进行优化,以获得最优聚类。采用标准的UCI数据集进行实验对比,发现改进后的算法相比传统的算法有较高的准确率和稳定性。     

基于新相似度的模糊协同聚类改进算法

提出一种优化传统协同聚类中模糊点类别归属的改进算法,该算法引入基于清晰半径的新相似性距离公式,用超球体中心区域代替传统算法中的类中心,在各子集初始聚类结果的基础上,对容易导致类别归属错误的模糊点重新计算隶属度,得到较为清晰的聚类结果。实验结果显示,改进算法能很大程度地减少边界上的模糊点个数及纠正分类错误,清晰半径的引入还能弱化各子集之间协同系数的差异,使得参数设置更为简单。     

基于改进主动表观模型算法的人脸特征定位

人脸特征点的精确定位一直是人脸图像处理的重要研究内容,特征点定位精确与否直接影响后续工作结果的好坏。在基于反向组合AAM（Active Appearance Models）人脸特征点定位算法的基础上,提出结合特征点局部纹理模型来对AAM初始形状参数做最优化以及对AAM匹配模板升级的改进。改进的算法采用特征点局部纹理模型和AAM全局纹理模型结合的方法来最优化AAM初始形状参数,并在此前提下对AAM匹配模板进行升级,使其更接近待匹配图像的信息。在精确的匹配模板和优化的初始形状参数下,匹配的最终精度会得到提升。实验和理论证明,改进后的算法比传统反向组合AAM算法以及现有改进的PAAM（Progressive AAM）算法以及简单的结合ASM和AAM的改进算法都有更好的特征点定位精度。     

Collaborative optimization with disciplinary conceptual design

For the first time, a multilevel optimization approach with disciplinary conceptual design is demonstrated. Collaborative optimization is used to decompose an example bridge design problem among two groups of designers – a superstructure design group and a deck design group. The disciplinary groups are allowed to search over different design concepts and formulate the design variables and constraints for each. The autonomy of the two groups is managed by a system-level group which insures that overall system objectives are met and coupling is properly accounted for. Even though discrete conceptual design occurs within the disciplinary groups, a continuous gradient-based optimization algorithm is used at the system level. The procedure was started from a nonoptimal concept, and converged to the optimal concept. Received September 9, 1999     

具有初始信息素的蚂蚁寻优软硬件划分算法

面向基于平台的系统芯片设计,提出具有初始信息素的蚂蚁寻优软硬件划分算法AOwIP．基本思想是：①利用基于平台的设计方法中已有参考设计的软硬件划分结果作为初始划分解,进行适当变换后生成初始信息素分布．②在所生成初始信息素分布的基础上,利用蚂蚁算法正反馈、高效收敛的优势寻求最优划分解．该算法利用基于平台的设计方法强调系统重用的优势,克服蚂蚁算法在求解软硬件划分问题时缺乏初始信息素的不足．实验表明,AOwIP算法有效提高了蚂蚁算法的最优解搜索效率．     

Managing approximation models in collaborative optimization

Collaborative optimization (CO), one of the multidisciplinary design optimization techniques, has been credited with guaranteeing disciplinary autonomy while maintaining interdisciplinary compatibility due to its bi-level optimization structure. However, a few difficulties caused by certain features of its architecture have been also reported. The architecture, with discipline-level optimizations nested in a system-level optimization, leads to considerably increased computational time. In addition, numerical difficulties such as the problem of slow convergence or unexpected nonlinearity of the compatibility constraint in the system-level optimization are known weaknesses of CO.This paper proposes the use of an approximation model in place of the disciplinary optimization in the system-level optimization in order to relieve the aforementioned difficulties. The disciplinary optimization result, the optimal discrepancy function value, is modeled as a function of the interdisciplinary target variables, and design variables of the system level. However, since this approach is hindered by the peculiar form of the compatibility constraint, it is hard to exploit well-developed conventional approximation methods. In this paper, neural network classification is employed as a classifier to determine whether a design point is feasible or not. Kriging is also combined with the classification to make up for the weakness that the classification cannot estimate the degree of infeasibility.In addition, for the purpose of enhancing the accuracy of the predicted optimum, this paper also employs two approximation management frameworks for single-objective and multi-objective optimization problem in the system-level optimization. The approximation is continuously updated using the information obtained from the optimization process. This can cut down the required number of disciplinary optimizations considerably and lead to a design (or Pareto set) near to the true optimum (or true Pareto set) of the system-level optimization.     

基于NSGA-II 的具有多目标子学科的协同优化方法

针对子学科具有物理目标的多目标协同优化问题, 研究基于NSGA-II 的求解策略. 鉴于子学科个体满足约束可行性的进化过程与系统级分配期望值无关, 提出具有良好的可行性和多样性的初始种群生成方法, 以提高多目标子学科的计算效率和计算精度. 为了解决由一致性目标函数与物理目标函数的作用不同而造成的NSGA-II 非支配级排序困难, 提出将子学科一致性目标函数转化为子学科自身约束的策略. 最后, 利用工程算例对所提出方法的有效性进行了验证.

     

改进蚁群算法在机器人路径规划中的应用

针对传统蚁群算法在移动机器人路径规划问题中存在的易陷入局部最优与收敛速度慢等问题,提出一种改进的蚁群算法。根据起点到终点距离和地图参数构建全局优选区域,提高该区域内初始信息素浓度,避免算法初期盲目搜素;利用局部分块优化策略分别对各个子区域进行寻优并更新区域内最优路径信息素,增强局部搜索能力,加快收敛速度;对全局路径进行寻优,更新全局最优路径信息素。在信息素更新公式中引入信息素增强因子,加强最优路径信息素含量,应用反向学习优化信息素,改进状态选择概率,提高算法寻优能力。实验结果表明,改进后的算法明显提高了收敛速度,同时寻优能力更强。     

基于混沌搜索的模糊控制器参数最优设计

基于混沌变量，本文提出一种模糊控制器最优设计方案．离线优化采用混沌算法，将混沌因子引入到模糊控制器参数域的优化搜索中，用载波方式将优化变量转变成混沌变量，再利用混沌运动的遍历性和随机性直接寻优，得到模糊控制器参数的全局次优解．在线优化采用共轭梯度下降法，把混沌搜索后得到的全局次优值作为梯度下降搜索的初始值，实现混沌全局粗搜索和梯度下降局部细搜索相结合的优化目的，能很快找到模糊控制器参数的全局最优解．最后对算法的收敛性进行了证明．     

A surrogate based multistage-multilevel optimization procedure for multidisciplinary design optimization

Optimization procedure is one of the key techniques to address the computational and organizational complexities of multidisciplinary design optimization (MDO). Motivated by the idea of synthetically exploiting the advantage of multiple existing optimization procedures and meanwhile complying with the general process of satellite system design optimization in conceptual design phase, a multistage-multilevel MDO procedure is proposed in this paper by integrating multiple-discipline-feasible (MDF) and concurrent subspace optimization (CSSO), termed as MDF-CSSO. In the first stage, the approximation surrogates of high-fidelity disciplinary models are built by disciplinary specialists independently, based on which the single level optimization procedure MDF is used to quickly identify the promising region and roughly locate the optimum of the MDO problem. In the second stage, the disciplinary specialists are employed to further investigate and improve the baseline design obtained in the first stage with high-fidelity disciplinary models. CSSO is used to organize the concurrent disciplinary optimization and system coordination so as to allow disciplinary autonomy. To enhance the reliability and robustness of the design under uncertainties, the probabilistic version of MDF-CSSO (PMDF-CSSO) is developed to solve uncertainty-based optimization problems. The effectiveness of the proposed methods is verified with one MDO benchmark test and one practical satellite conceptual design optimization problem, followed by conclusion remarks and future research prospects.     

An augmented Lagrangian decomposition method for quasi-separable problems in MDO

Several decomposition methods have been proposed for the distributed optimal design of quasi-separable problems encountered in Multidisciplinary Design Optimization (MDO). Some of these methods are known to have numerical convergence difficulties that can be explained theoretically. We propose a new decomposition algorithm for quasi-separable MDO problems. In particular, we propose a decomposed problem formulation based on the augmented Lagrangian penalty function and the block coordinate descent algorithm. The proposed solution algorithm consists of inner and outer loops. In the outer loop, the augmented Lagrangian penalty parameters are updated. In the inner loop, our method alternates between solving an optimization master problem and solving disciplinary optimization subproblems. The coordinating master problem can be solved analytically; the disciplinary subproblems can be solved using commonly available gradient-based optimization algorithms. The augmented Lagrangian decomposition method is derived such that existing proofs can be used to show convergence of the decomposition algorithm to Karush–Kuhn–Tucker points of the original problem under mild assumptions. We investigate the numerical performance of the proposed method on two example problems.     

采用遗传算法的文本无关说话人识别

为解决在说话人识别方法的矢量量化(Vector Quantization,VQ)系统中,K-均值法的码本设计很容易陷入局部最优,而且初始码本的选取对最佳码本设计影响很大的问题,将遗传算法(Genetic Algorithm,GA)与基于非参数模型的VQ相结合,得到1种VQ码本设计的GA-K算法.该算法利用GA的全局优化能力得到最优的VQ码本,避免LBG算法极易收敛于局部最优点的问题;通过GA自身参数,结合K-均值法收敛速度快的优点,搜索出训练矢量空间中全局最优的码本.实验结果表明,GA-K算法优于LBG算法,可以很好地协调收敛性和识别率之间的关系.     

一种改进K-means聚类的FCMM算法

针对K-means算法易受初始聚类中心影响而陷入局部最优的问题,提出一种基于萤火虫智能优化和混沌理论的FCMM算法。首先利用最大最小距离算法确定聚类类别值K和初始聚类中心位置;然后以各聚类中心为基准点,利用Tent映射构建混沌空间,通过混沌搜索更新聚类中心,以降低初始聚类中心过于临近的影响,并改善算法易陷入局部最优的问题。仿真结果表明,FCMM算法的平均聚类精度相较于经典K-means算法和FA算法分别提高了7.51%和2.2%,成功避免算法陷入局部最优解,提高了划分初始数据集的效率和寻优精度。