大展弦比运输机标模高升力构型设计与评估

介绍了中国航空工业空气动力研究院基于分层策略遗传算法的多段翼型优化设计工具和高升力构型计算评估方法,参照国内外大展弦比运输机指标为FL-9增压风洞设计了高升力构型标模,为检验标模气动性能,在法国ONERA F1风洞进行了一系列风洞试验和采用UNSMB平台进行了数值模拟。结果显示计算结果与试验结果有着较好的一致性,设计方案具有较好的增升效果。     

一种非线性飞行控制律评估方法研究

研究了一种基于改进遗传算法的非线性评估方法。将非线性准则转换成简单的适应度函数,通过寻优找到最坏情况,在其基础上实现评估。仿真结果表明,该方法不仅能在各个飞行状态对飞行控制律进行非线性准则评估,而且能找出飞行包线内的最坏飞行状态,细化飞行包线,方法使用灵活,结果可靠。     

基于遗传算法的军事物流运输计划

针对军事物流运输中车辆装载和车辆路径的组合问题进行研究,建立车辆装载和车辆路径组合问题的目标优化模型。通过改进遗传算法对模型求解,得到了较为满意的结果,可以在满足多车型多品种货物配送约束的条件下,实现运输车辆最少、车辆满载率高、车辆运输路径最短的目标。     

基于遗传算法的成人高等教育智能组卷模型的研究与设计

为建立成人高等教育智能组卷模型，利用遗传算法和函数矩阵，对现有组卷系统进行了优化设计，并提出了针对性的改进方案，为成人高等教育的考试组卷提供了适应的技术支持。     

Improved NSGA-II Multi-objective Genetic Algorithm Based on Hybridization-encouraged Mechanism

To improve performances of multi-objective optimization algorithms, such as convergence and diversity, a hybridization- encouraged mechanism is proposed and realized in elitist nondominated sorting genetic algorithm （NSGA-Ⅱ）. This mechanism uses the normalized distance to evaluate the difference among genes in a population. Three possible modes of crossover operators--＂Max Distance＂, ＂Min-Max Distance＂, and ＂Neighboring-Max＂--are suggested and analyzed. The mode of ＂Neighboring-Max＂, which not only takes advantage of hybridization but also improves the distribution of the population near Pareto optimal front, is chosen and used in NSGA-Ⅱ on the basis of hybridization-encouraged mechanism （short for HEM-based NSGA-Ⅱ）. To prove the HEM-based algorithm, several problems are studied by using standard NSGA-Ⅱ and the presented method. Different evaluation criteria are also used to judge these algorithms in terms of distribution of solutions, convergence, diversity, and quality of solutions. The numerical results indicate that the application of hybridization-encouraged mechanism could effectively improve the performances of genetic algorithm. Finally, as an example in engineering practices, the presented method is used to design a longitudinal flight control system, which demonstrates the obtainability of a reasonable and correct Pareto front.     

基于遗传算法的地磁匹配搜索策略

为降低导航成本,提出仅利用磁测计和里程计的地磁场匹配定位方法.为解决搜索空间过大的问题,提出以遗传算法在匹配区域搜索可能路径的方法.该方法以相关性度量作为适应度函数,交叉与变异仅针对点列的相对关系完成,提出用局部适应度函数自适应调整交叉概率与变异概率.仿真结果表明,该方法可以有效完成地磁匹配导航中任意形状路径的匹配.     

基于模糊机会约束规划的机会阵雷达方向图综合

针对机会阵雷达方向图综合的不确定问题,提出了一种基于模糊机会约束规划的方向图综合规划模型。该模型基于可信性理论,综合考虑天线单元分布的随机性以及激励状态的不确定性,将参与方向图综合时激励打开的天线数目看做一个梯形模糊变量,用以描述综合时的复杂不确定环境。随后,将规划模型转化为清晰等价形式,再结合遗传算法和灰关联综合评价法则设计了一种混合智能优化算法,用于求解该模型。以一维任意非均匀线阵为例,对主瓣宽度和最大副瓣电平进行了优化。仿真表明:优化后结果的可信性高于置信水平,验证了该算法的有效性。     

A strategic flight conflict avoidance approach based on a memetic algorithm

Conflict avoidance （CA） plays a crucial role in guaranteeing the airspace safety. The cur- rent approaches, mostly focusing on a short-term situation which eliminates conflicts via local adjust- ment, cannot provide a global solution. Recently, long-term conflict avoidance approaches, which are proposed to provide solutions via strategically planning traffic flow from a global view, have attracted more attentions. With consideration of the situation in China, there are thousands of flights per day and the air route network is large and complex, which makes the long-term problem to be a large-scale combinatorial optimization problem with complex constraints. To minimize the risk of premature convergence being faced by current approaches and obtain higher quality solutions, in this work, we present an effective strategic framework based on a memetic algorithm （MA）, which can markedly improve search capability via a combination of population-based global search and local improve- ments made by individuals. In addition, a specially designed local search operator and an adaptive local search frequency strategy are proposed to improve the solution quality. Furthermore, a fast genetic algorithm （GA） is presented as the global optimization method. Empirical studies using real traffic data of the Chinese air route network and daily flight plans show that our approach outper- formed the existing approaches including the GA .based approach and the cooperative coevolution based approach as well as some well-known memetic algorithm based approaches.     

基于GA-ELM的飞行载荷参数识别

针对用复杂飞行数据识别飞行载荷时的精度低、速度慢等问题,提出一种结合遗传算法(GA)和极限学习机(ELM)的GA-ELM模型。该模型使用ELM神经网络作为计算核心,用遗传算法产生ELM网络输入层到隐含层的权值矩阵和隐含层偏移量;用GA-ELM模型对飞行数据进行识别,并与BP神经网络和原始ELM神经网络的识别结果进行对比。结果表明:GA-ELM模型是一种有效且高精度的飞行载荷参数识别方法。