Adaptive double chain quantum genetic algorithm for constrained optimization problems

Optimization problems are often highly constrained and evolutionary algorithms(EAs)are effective methods to tackle this kind of problems. To further improve search efficiency and convergence rate of EAs, this paper presents an adaptive double chain quantum genetic algorithm(ADCQGA) for solving constrained optimization problems. ADCQGA makes use of doubleindividuals to represent solutions that are classified as feasible and infeasible solutions. Fitness(or evaluation) functions are defined for both types of solutions. Based on the fitness function, three types of step evolution(SE) are defined and utilized for judging evolutionary individuals. An adaptive rotation is proposed and used to facilitate updating individuals in different solutions.To further improve the search capability and convergence rate, ADCQGA utilizes an adaptive evolution process(AEP), adaptive mutation and replacement techniques. ADCQGA was first tested on a widely used benchmark function to illustrate the relationship between initial parameter values and the convergence rate/search capability. Then the proposed ADCQGA is successfully applied to solve other twelve benchmark functions and five well-known constrained engineering design problems. Multi-aircraft cooperative target allocation problem is a typical constrained optimization problem and requires efficient methods to tackle. Finally, ADCQGA is successfully applied to solving the target allocation problem.     

基于Nash-Pareto策略的两种改进算法及其应用

针对多目标、多设计变量的优化问题,提出了两种优化的新算法:一种是将多目标问题转化为单目标时,对目标权重的确定提出了新的途径;另一种是直接对多目标问题进行优化,并对Pareto遗传优化技术作了改进,以得到均匀分布的Pareto最优解集.两种新算法都是建立在Nash的系统分解与Pareto遗传算法的基础上,因此称这类算法为Nash-Pareto策略.借助于这类算法,文中以跨声速压气机双圆弧类叶型的气动优化为例,给出了气动优化的全过程.数值优化的实验表明所给出的改进算法是可行的、有效的.     

随机振动响应下的组件结构布局优化设计

 从工程应用需求出发,研究了随机振动载荷作用下的组件结构布局优化问题。提出采用有限包络圆族描述组件外形轮廓的近似方法,有效解决了设计过程中预防组件干涉的问题;建立了随机振动响应分析有限元模型,并对模型进行了实验验证与结果分析。在此基础上,以随机振动载荷下特定点自由度响应均方根（RMS）值之和最小为目标函数建立了组件布局通用优化设计模型,研究了单一组件与4组件问题的布局优化问题,并分别采用梯度优化算法和遗传算法进行优化求解比较研究。结果表明,所提出的设计方法能够有效地实现随机振动响应下的组件结构布局优化设计,两种优化算法均能显著降低结构关键部位的动力学响应。     

分布式协同进化MDO算法及其在导弹设计中应用

 针对现有基于梯度的多学科设计优化 (MDO)算法不适用于具有离散和整数设计变量、设计空间非凸或不连通的多学科设计优化问题,以及倾向于收敛到接近初始点的局部最优点的缺点,为充分发挥进化算法的优越性,根据协同进化与 MDO在本质上的相似性,采用分布式协同进化机制进行 MDO算法研究。提出了分布式协同进化 MDO算法,并将该算法应用于导弹的气动 /发动机 /控制一体化优化设计。     

基于信任域策略优化的末制导控制量学习算法CSCD

近年来,深度强化学习在解决序列决策问题上取得了很大进展,无模型强化学习算法在与环境不断交互的过程中学习策略,不需要提前对环境建模,使其适用于许多问题。针对以往使用强化学习进行末制导策略学习的训练不稳定问题,使用信任域策略优化算法直接学习末制导控制量,同时设计了一种新颖的奖励函数,可以提高训练稳定性和算法性能。在二维环境下进行了实验,结果表明,该算法具有良好的训练稳定性,并可以达到很好的命中效果。     

基于灵敏度分析的飞行器稳健设计优化方法

提出了一种基于灵敏度分析的稳健设计优化方法。针对设计变量存在扰动的不确定性问题,在原有优化数学模型上,增加了准则函数和约束函数的灵敏度附加项。采用全局灵敏度方程来计算准则函数及约束函数对设计变量的灵敏度,进而得出新的优化问题数学模型,得到问题的稳健性解。将上述方法应用到轻型飞机的总体设计中,对飞机重量、气动和性能模块进行多目标优化,得出最优解,并与已有的飞机进行对比和分析。     

Automatic Target Recognition: State of the Art Survey

In this paper a review of the techniques used to solve the automatic target recognition (ATR) problem is given. Emphasis is placed on algorithmic and implementation approaches. ATR algorithms such as target detection, segmentation, feature computation, classification, etc. are evaluated and several new quantitative criteria are presented. Evaluation approaches are discussed and various problems encountered in the evaluation of algorithms are addressed. Strategies used in the data base design are outlined. New techniques such as the use of contextual cues, semantic and structural information, hierarchical reasoning in the classification and incorporation of multisensors in ATR systems are also presented.     

分布式进化算法及其在翼型气动反设计中的应用

构造了一种新型基于基因算法与博弈论的并行分级多目标优化方法，并应用于多段翼型气动反设计。此方法基于二进制编码的基因算法和博弈论，优化变量被分配给不同的博弈者，因而总体优化问题转变为分裂空间中的局部优化问题。文中给出了一个多段翼型形状，位置可压位流的反设计问题的求解算例，引入了基于非结构网格的分级结构。与传统基因算法数值算例的对比表明了本文构造的并行分级算法具有较高的计算效率，可广泛应用于多目标优化问题。     

联合截获威胁下的雷达射频隐身目标搜索算法

针对基于射频(RF)隐身需求的机载雷达目标搜索问题进行了研究。通过分析实际作战中射频辐射面临的截获威胁,提出了一种基于联合截获威胁的射频隐身性能表征方法,并给出了具体的值估算方法;对机载雷达搜索任务中辐射参数的优化问题进行了建模,并采用优化算法对目标函数进行求解。通过分析典型情景下解集的分布特点,给出了从最优解集中选取最终解的方法。结果表明,提出的射频隐身性能表征方法能更好地反映截获实现过程的多域需求,提出的雷达搜索方法能够在保证探测性能的同时,提高射频隐身性能和搜索速度,能够为相控阵雷达搜索任务中参数的优化控制提供方法和依据。     

多段翼型气动力优化设计的多标准高效进化算法

本文分别结合基因算法与博弈论中的竞争型Nash对策及分级型Stackelberg对策构造了多标准高效进化算法,并对二维三段翼型成功地进行了多标准位置增升优化。基因算法在合适地选择了基因操作算子后可以得到全局最优解,博弈论的引入使得传统基因算法具备了无人工干预的多标准优化算法,并且这一优化过程接近于实际翼型工程气动力设计情况。应用本文的方法成功地对三段翼型进行了不同气动条件下的缝翼与襟翼的位置组合优化,最后给出了优化算例,并对两种算法进行了对比。     

Stochastic structural optimization using particle swarm optimization, surrogate models and Bayesian statistics

This paper focuses on a method to solve structural optimization problems using particle swarm optimization (PSO), surrogate models and Bayesian statistics. PSO is a random/stochastic search algorithm designed to find the global optimum. However, PSO needs many evaluations compared to gradient-based optimization. This means PSO increases the analysis costs of structural optimization. One of the methods to reduce computing costs in stochastic optimization is to use approximation techniques. In this work, surrogate models are used, including the response surface method (RSM) and Kriging. When surrogate models are used, there are some errors between exact values and approximated values. These errors decrease the reliability of the optimum values and discard the realistic approximation of using surrogate models. In this paper, Bayesian statistics is used to obtain more reliable results. To verify and confirm the efficiency of the proposed method using surrogate models and Bayesian statistics for stochastic structural optimization, two numerical examples are optimized, and the optimization of a hub sleeve is demonstrated as a practical problem.     

一种新的求解TSP问题的杂交算子

TSP(TravelingSalesmanProblem)问题是最经典的NP-hard组合优化问题之一。长期以来,人们一直在寻求快速、高效的近似算法,以便在合理的时间内解决大规模问题。在文[5]提出的两交换启发交叉算子的基础上,通过分析,发现该算子的杂交结果与所选择的首城市有关,因而不同的首城市的选择会大大影响该算子的效率。为此,提出了一种新的有效利用局部信息的杂交算子,该算子能够有效的保存母体信息,进一步摆脱首城市的选择问题。实例仿真证明该算子的有效性。     

Application of multi-objective optimization to axial compressor preliminary design

The axial compressor is one of the most challenging components for aero engine design. The highly complex and multi-disciplinary design process is built up from several separate design phases differing with respect to the number of details. Typically, meanline prediction is the first step of the aerodynamic design process where the goal is to provide a first guess and proper choice of basic design parameters. As a preliminary design procedure it is one of the most important parts of the compressor design process since a poor design decision on these parameters cannot be corrected by subsequent development efforts. The design of a compressor is always a compromise between contradicting requirements like high efficiency, low number of stages and high surge margin. This is typical for multi-criterion optimization problems requiring a high number of design analyses and a well-designed procedure for finding trade-off solutions. The paper will show how to automate a given Rolls-Royce preliminary design process in order to find Pareto-optimal trade-off solutions for design conditions. The aspect of process acceleration is also an important goal to release the design engineer from time-consuming parameter studies. Essential elements for speeding up the design process are the use of modern process integration tools, multi-criterion decision concepts, and nonlinear programming algorithms. Results will be shown based on a given Rolls-Royce compressor design for multi-objective optimization with respect to maximum efficiency, maximum surge margin, and maximum overall pressure ratio, where different deterministic and stochastic algorithms are used.     

Performance comparison of PRF schedules for medium PRF radar

Previous work has shown how evolutionary algorithms (EAs) are an effective tool in optimising the selection of pulse repetition frequency (PRF) values of medium PRF schedules in an airborne fire control radar (FCR) application requiring target data in three PRFs. The optimisation is driven by the requirement to minimise range/Doppler blindness whilst maintaining full decodability. In this paper we detail work in which the optimisation process is applied to design novel short medium PRF schedules requiring target data in just two PRFs. The paper reports on the testing of a variety of near-optimum schedules to compare their blindness, decoding, and ghosting performances. The results show that in many situations, the 2 of N schedules are a practical alternative to conventional 3 of N processing.     

通用飞行器气动优化设计数字化集成平台——DIPasda

未来航空工业的发展，需要解决多学科综合设计的关键问题，为新型高性能飞行器的设计提供有力的设计方法和设计工具。DIPasda作为复杂外形设计的通用飞行器多学科优化设计平台，研制目的主要是提供一套新型通用、鲁棒、高效的优化设计架构，应用于通用飞行器工业设计环境，改善传统设计耗时低效的状况，提高新型飞行器设计的效率和精度。DIPasda平台系统包含了优化设计过程中所需用到的各类方法，主要包括数值优化算法、几何模型参数化方法、代理模型方法、高精度的学科分析工具等。通过详细介绍平台的系统架构、主要的功能模块、伴随优化设计和多目标优化设计流程，展现了DIPasda平台系统架构设计的灵活性和功能模块的完备性。最后通过优化算例展示了系统的综合优化设计能力。     

超临界机翼多目标气动优化设计的策略与方法

超临界机翼的气动设计十分复杂,往往需要借助于有效的优化手段。然而,优化方法本身往往无法直接表达真实完整的设计意图,因此需要将工程需求和约束转换为优化方法所能处理的数学形式。本文首先探讨了梯度优化方法和进化类优化方法在气动优化中的表现。之后简要总结了前人在飞机气动设计中提出的准则和要求,并基于压力分布形态定义了超临界机翼气动设计的关键特征及定量要求,探究其在超临界机翼气动优化设计中的应用效果。研究结果表明,在多目标优化中引入面向压力分布形态特征的目标和约束,能有效引导优化体现工程设计思想,进而提高优化效率。     

翼型气动优化设计问题多极值特性研究

认识翼型气动外形优化问题设计空间的多极值特性,有助于人们在翼型设计阶段选择合理的优化算法,提高优化效率、缩短设计周期。研究RAE2822 翼型在优化减阻过程中设计空间的多极值特性,采用ADODGcase2算例,使用自由变形方法(FFD)对翼型进行参数化,通过拉丁超立方抽样方法对翼型加入初始扰动;使用基于梯度的优化算法对经过不同扰动后的翼型进行优化,并将优化结果与全局优化算法的优化结果进行对比。结果表明:ADODGcase2可能是一个单峰值气动设计问题,梯度算法能够得到相对满意的最优解,并且具有更高的优化效率;在给定面积约束的条件下,对于翼型跨声速单点减阻优化问题,设计空间很可能是单峰值的,可直接使用梯度优化算法。     

基于坐标关联法的榫连接结构优化设计

为解决结构优化设计过程中由于几何尺寸干涉而导致的优化过程中断问题,提出并建立一种适于优化设计的结构建模方法——坐标关联法.将该方法应用于航空发动机榫连接结构的优化设计,整个优化过程顺畅,连续完成几何建模790次,未出现由于几何尺寸改变而发生的结构模型无法生成的问题.优化设计结果表明:优化前后的榫连接结构形状虽然变化不大,但最大应力点——榫槽喉部最大应力降幅达19.1%,应力水平降低明显,且结构造型符合工程要求.      

针对机载环境应用的视频编码器的优化与实现

本文针对机载航空视频记录的特点,介绍了在 TMS320DM6467平台上实现 H.264视频编码器的算法设计与优化方法.在软件整体设计优化的基础上,结合 TMS320DM6467的平台特点,给出了一套结构优化方法,主要包括提高代码并行性及高效利用协处理器 HDVICP 的算法运算子模块.测试结果表明,通过优化,在保证图像质量损失较小的情况下,编码器的编码速率有显著提高     

协同优化在机翼气动／结构一体化设计中初步应用

探讨协同优化方法是否能有效地解决机翼气动 结构一体化设计优化问题。首先对基本的协同优化和基于响应面协同优化二种方法的特点进行了探讨,然后以轻型飞机机翼气动 结构一体化设计为例,着重研究如何用协同优化方法建立机翼气动 结构一体化设计的优化模型。研究结果表明,基本的协同优化算法不能有效地解决该机翼气动 结构一体化优化问题,而基于响应面的协同优化方法在求解这一问题时具有较好的鲁棒性。