基于STK的卫星系统设计优化研究

随着小卫星技术的发展，卫星系统的结构和功能也越来越复杂。为了降低卫星系统的造价，提高卫星系统的性能，需要对卫星系统进行设计优化。该文针对目前通用设计软件的不足，提出在STK和ModelCenter的基础上。开发卫星系统设计优化平台，详细阐述了基于STK仿真环境的卫星系统设计优化过程。为实现卫星系统的设计优化，应在任务分析的基础上定义STK场景，确定卫星系统的性能评价指标和设计变量，对设计变量进行敏感性分析，选择关键的设计变最，确定优化目标函数和约束条件并进行优化求解。最后给出了一个卫星系统设计优化的应用实例。     

Matlab优化工具箱在钻臂设计中的应用

Matlab优化工具箱具有强大的科学计算能力，在工程设计领域得到了广泛的应用。基于优化设计中Kuhn-Tucker（K-T）方程解的原理，本文给出了利用Matlab优化工具箱进行钻臂设计的方法。首先，合理设置优化目标函数和约束条件。然后，使用Matlab优化工具箱进行编程计算。与其他方法相比，使用Matlab优化工具箱进行优化，不仅可以提高计算精度，而且可以减少计算时间，因此在工程设计领域有较强的实际应用价值。     

基于串联协作的多方法协作优化方法

为更好地求解复杂优化问题的全局最优解，提出了基于串联协作的多方法协作优化方法。它采用串联方式组织各个优化方法之间的协作。各个优化方法在每次协作中进行若干次优化，每一优化方法在优化过程中及优化结果中包含的优化信息被用于其它优化方法中，以提高整体优化性能。选择了遗传算法、模式搜索法和Powell法等三个直接优化方法进行串联协作组成的多方法协作优化方法的设计，并对其优化特性进行了深入的理论分析。最后，用三个复杂多维函数对算法性能进行了测试。计算实例表明，基于串联协作的多方法协作优化方法取得了优于单个优化方法的全局最优特性。     

3G CDMA2000基站网管系统信令跟踪的设计与优化

文章阐述了3G CDMA2000网管中网元侧信令跟踪的设计及其优化过程，对软件优化的相关问题进行了探讨，比如如何发现软件需要优化的地方，如何进行优化，如何充分利用已有的开发经验等，并针对这些问题提出了一系列相应的解决方法和措施，这些经验和方法对软件优化工作有一定的参考价值。     

基于遗传算法的CBTC系统AP部署优化问题研究

为进一步提高CBTC系统的信息传输稳定性，基于遗传算法对CBTC系统的AP部署优化问题进行研究，以实现对AP部署的进一步优化。其中，AP部署的优化方法选择精确中断概率法；选择改进的遗传算法对AP部署优化的搜索方法进行优化。仿真结果表明，改进后的遗传算法能够实现更快的收敛，同时能够保持较低的相对误差和较快的运行速度。与未改进的遗传算法相比，改进的遗传算法的相对误差和运行速度分别为前者的0.125倍和100倍，验证了该算法的优越性。以上结果验证了本研究进行优化的可行性和必要性，具有一定的实际参考价值。     

微器件设计的优化决策规则获取方法研究

针对微器件产品优化评价规则缺乏的现状，采用现代设计方法对微器件优化决策规则进行了研究，有限元方法被用于对微器件进行动态分析，获取大量的信息数据；粗糙集理论和遗传算法被用于属性约简和优化决策规则提取，根据以上方法对相应原型软件进行开发，对热驱动微泵实例进行应用示范，为微器件优化决策规则制定提供了方法和原型软件。     

基于变尺度混沌优化方法的PID控制器设计

混沌优化方法是一种新兴的优化方法，它利用混沌系统具有的随机性，遍历性和对初值的极端敏感性等特点实现优化搜索，尤其对目标函数不可微，不连续的多参数寻优问题十分有效，把变尺度混沌优化方法引入到PID控制器的设计中，对PID参数进行优化并用SIMULINK对优化结果进行了仿真，结果证明，变尺度混沌优化方法算法简单有效，所设计的PID控制系统具有响应快，超调小和控制精度高等优点，具有很好的实用价值。     

考虑无人机飞行时间的能耗与数据量联合优化

在无线传感网络中运用无人机（unmanned aerial vehicle,UAV）通信进行数据采集是一项有价值的技术。针对UAV在有限时间内的数据采集任务，提出了一种考虑系统数据量、节点传输能耗和UAV飞行能耗的联合优化方案。该方案的决策空间包括UAV轨迹与传输调度，复杂度较高。由于联合优化是NP难问题，基于决策空间降维，将优化过程分为初始轨迹优化和二次轨迹优化两步。针对初始轨迹优化，提出基于贪心算法和禁忌搜索算法的优化方案，实现节点选择并得到UAV初始轨迹；针对二次轨迹优化，运用离散化方法转换原问题，采用逐次凸逼近算法进行优化，得到其有效次优解。仿真结果表明，所提优化方案能够在满足时间约束的前提下，提高UAV采集的数据量，并降低UAV和节点的能耗。     

仿真优化研究进展

仿真优化研究基于仿真的目标优化问题，具有很强的工程背景，但存在随机性、计算费时和NP—hard等难点。目前，仿真优化已成为国际上系统仿真和运筹学等领域共同关注的热点和前沿课题，尤其是在离散事件动态系统领域。因此在分析仿真优化特点的基础上，对仿真优化算法进行了综述，介绍了仿真优化的若干改进、应用和软件，并指出了仿真优化的进一步研究内容和方向。     

基于方向距离顺序处理的路线优选算法改进

介绍了从距离和方向角度出发对最短路径算法进行了优化，提出障碍物规避原则基础上的路线选择办法，从距离优化和方向优化着手依据路径选择的特点，形成促成算法改进的基本思想和原理。阐述了优化后算法的实现过程和原理，最后通过实例证明了优化后的算法便于使用，提高了计算效率。     

A global optimization method for nonlinear bilevel programmingproblems

Nonlinear two-level programming deals with optimization problems in which the constraint region is implicitly determined by another optimization problem. Mathematical programs of this type arise in connection with policy problems to which the Stackelberg leader-follower game is applicable. In this paper, the nonlinear bilevel programming problem is restated as a global optimization problem and a new solution method based on this approach is developed. The most important feature of this new method is that it attempts to take full advantage of the structure in the constraints using some recent global optimization techniques.     

一种求解非线性约束优化全局最优的新方法

本文提出了一种求解非线性约束优化的全局最优的新方法—它是基于利用非线性互补函数和不断增加新的约束来重复解库恩-塔克条件的非线性方程组的新方法。因为库恩-塔克条件是非线性约束优化的必要条件,得到的解未必是非线性约束优化的全局最优解,为此,本文首次给出了通过利用该优化问题的先验知识,不断地增加约束来限制全局最优解范围的方法,一些仿真例子表明提出的方法和理论有效的,并且可行的。     

一种 新的Cache优化方法—部分Cache局部性方法

Ｃａｃｈｅ的性能优化在高性能计算中起着非常重要的作用。传统的Ｃａｃｈｅ优化方法存在着一些缺陷。本文分析ＲＩＳＣ处理器的特点的基础 上，提出了“部分ｃａｃｈｅ方法。实践表明，该方法有很好的优化效果，且易实于实现。     

A poly-hybrid PSO optimization method with intelligent parameter adjustment

This paper presents results on a new hybrid optimization method which combines the best features of four traditional optimization methods together with an intelligent adjustment algorithm to speed convergence on unconstrained and constrained optimization problems. It is believed that this is the first time that such a broad array of methods has been employed to facilitate synergistic enhancement of convergence. Particle swarm optimization is based on swarm intelligence inspired by the social behavior and movement dynamics of bird flocking, fish schooling, and swarming theory. This method has been applied for structural damage identification, neural network training, and reactive power optimization. It is also believed that this is the first time an intelligent parameter adjustment algorithm has been applied to maximize the effectiveness of individual component algorithms within the hybrid method. A comprehensive sensitivity analysis of the traditional optimization methods within the hybrid group is used to demonstrate how the relationship among the design variables in a given problem can be used to adjust algorithm parameters. The new method is benchmarked using 11 classical test functions and the results show that the new method outperforms eight of the most recently published search methodologies.     

一种新的有限元网格优化方法

本文仔细探讨了平面网格产生畸形单元的原因,并针对性地提出了“结构优化”的概念,将其与传统的“位置优化”区别开来。本文介绍的方法通过结构优化与位置优化相结合对初始有限元网格进行调整,使最终的网格最大限度地满足有限元分析的需要,从而提出了一种能够较为彻底地提高网格质量的新的网格优化方法。     

A multi-point sampling method based on kriging for global optimization

In general, sampling strategy plays a very important role in metamodel based design optimization, especially when computationally expensive simulations are involved in the optimization process. The research on new optimization methods with less sampling points and higher convergence speed receives great attention in recent years. In this paper, a multi-point sampling method based on kriging (MPSK) is proposed for improving the efficiency of global optimization. The sampling strategy of this method is based on a probabilistic distribution function converted from the expected improvement (EI) function. It can intelligently draw appropriate new samples in an area with certain probability according to corresponding EI values. Besides, three strategies are also proposed to speed up the sequential sampling process and the corresponding convergence criterions are put forward to stop the searching process reasonably. In order to validate the efficiency of this method, it is tested by several numerical benchmark problems and applied in two engineering design optimization problems. Moreover, an overall comparison between the proposed method and several other typical global optimization methods has been made. Results show that the higher global optimization efficiency of this method makes it particularly suitable for design optimization problems involving computationally expensive simulations.     

A hybrid method of chaotic particle swarm optimization and linear interior for reactive power optimisation

The reactive power optimization is an effective method to improve voltage level, decrease network losses and maintain the power system running under normal conditions. This paper provides a method combining particle swarm optimization (PSO) with linear interior point to handle the problems remaining in the traditional arithmetic of time-consuming convergence and demanding initial values. Furthermore, since chaotic mapping enjoys certainty, ergodicity and stochastic property, the paper introduces chaos mapping into the particle swarm optimization, the paper presents a new arithmetic based on a hybrid method of chaotic particle swarm optimization and linear interior point. Thanks to the superior overall exploration ability of particle swarm optimization and the local exploration ability of linear interior point within the neighborhood of the optimal point, the new method can improve the performance of both convergence and results’ precision. Tested by IEEE-30, the new method provided in this paper is proved effective and practical in the optimization of shunt capacitors and tap position of load-ratio voltage transformer.     

一类动态非线性约束优化问题的新解法

动态非线性约束优化是一类复杂的动态优化问题,其求解的困难主要在于如何处理问题的约束及时间（环境）变量。给出了一类定义在离散时间（环境）空间上的动态非线性约束优化问题的新解法,从问题的约束条件出发构造了一个新的动态熵函数,利用此函数将原优化问题转化成了两个目标的动态优化问题。进一步设计了新的杂交算子和带局部搜索的变异算子,提出了一种新的多目标优化求解进化算法。通过对两个动态非线性约束优化问题的计算仿真,表明该算法是有效的。     

二维限定三角网格质量优化方法

使用拓扑优化和几何优化相结合的方法对限定Delaunay三角网格进行质量优化,首先针对普通的拉普拉斯算法会造成网格局部收缩的缺陷,对拉普拉斯算子进行了改进,改进后的拉普拉斯算子克服了这一缺陷;然后对网格顶点的度进行优化,使每个顶点都有一个优化的度数;最后对拓扑优化的网格使用改进的拉普拉斯算子进行几何优化,优化后的网格质量得到了显著的提高,算例表明了算法是可行的。     

A mathematical model of higher level structural optimization problems and their solution

In this paper, a new mathematical model suitable for higher level structural optimization problems, such as optimization of structural topology, layout and type is presented. In this mathematical model, the relation between two structures with different layouts is established by introducing the nonbasic variables. Using the Kuhn-Tucker condition for optimality, a criterion for determining a better layout of a structure is developed. This provides a measure for selecting the optimal layout of a structure. The method introduces a new way for higher level structural optimization design. Several numerical examples are given to illustrate the effectiveness of this method.