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.     

基于复合弯掠技术的轴流风机叶轮优化

基于叶轮机械全三维优化设计平台NUMECA/Design 3D,采用人工神经网络和遗传算法相结合的方法,通过复合弯掠技术对一低压轴流风机叶轮空间弯掠积叠线进行三维优化设计。优化目标为在设计工况点保持风机流量不变,尽可能提高气动效率和全压升。结果表明:复合弯掠优化使叶轮设计工况点的效率和全压升分别提升约1.92%和3.98%。复合弯掠优化使叶片负荷重新分布,改善了叶顶和叶根区域的流动,同时抑制了叶轮近吸力面叶根角区的流动分离和减小了叶顶泄漏涡的强度和影响范围,降低了流动损失,提高了叶轮气动性能。     

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.     

多跑道航班进离场动态调度模型与算法

针对民航运输快速发展导致的航班延误频增现象,研究了多跑道航班进离场的动态调度问题。研究时段内航班的总延误成本最小和延误时间均衡为目标,综合考虑根据机型确定的航班进离场调度优先权和管制员负荷,建立多跑道航班进离场调度模型,利用遗传算法对模型进行仿真验证。仿真结果与先到先服务(FCFS)调度方式进行比较,采用遗传算法的航班进离场调度比FCFS的延误成本降低了45.07%,延误时间降低了37.90%,同时有效地均衡了航空公司的延误时间,保障了航空公司的公平性并提高了跑道系统容量,降低了管制员负荷。通过仿真验证了多跑道航班进离场动态调度策略具有较强的优势和可行性。     

单扇区管制移交间隔管理模型和算法研究

针对区域管制移交间隔管理问题进行研究,通过分析当前的流量管理模型和算法,提出单个扇区管制移交间隔管理模型。在研究中,引入间隔矩阵概念,以指定时段内的延误控制为目标,分析扇区容量和管制移交高度层选择等限制因素,建立数学模型,并通过间隔增量矩阵的使用来优化算法。算例中采用在国内某区域管制范围内的繁忙时段数据进行了分析和验证。     

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。     

基于遗传算法的护理机器人逆运动学求解方法

护理机器人结构复杂、耦合度高且机械臂不满足PIEPER准则,标准遗传算法难以精准地对其逆运动学进行求解,以至于机器人手臂末端位姿误差较大。针对标准遗传算法求解过程中早熟和局部搜索能力差的问题,采用等分区间替代随机命令产生的初始种群个体,划分5个小区间以提高种群个体的分散程度和搜索效率;在适应度函数中引入可变权因子,将位置误差的变化值作为可变权因子的变量,进化过程中可变权因子在0.5~1.0之间变化,且实时有效分配位置和姿态误差权重,确保解的收敛。通过仿真和实验验证,结果表明:改进后的遗传算法能够大幅提升收敛精度和速度,并且可以同时实现对位置和姿态的精确控制,极大地减小了机器人手臂的位姿误差。      

考虑制造约束的印刷电路板式换热器优化设计

针对印刷电路板式换热器(PCHE)的微流道结构参数设计制造一体化问题,开展了考虑制造约束条件的换热器性能优化的研究。通过流体仿真分析了流道宽度、深度和深宽比对温度分布、压力损失和换热系数的影响。使用多目标遗传算法(MOGA)建立了换热性能的多参数多目标性能优化仿真模型,根据性能优化获得的设计参数建立微流道结构辊压成形工艺仿真模型,得到制造约束条件,将制造约束反馈到性能优化仿真模型中,得到宽为0.29 mm、深为0.39 mm的矩形微流道优化设计参数,且通过辊对辊(R2R)的辊压工艺实验验证了该优化方法的可行性。考虑制造约束的换热器优化设计方法在性能优化设计阶段引入工艺限制条件,是实现换热器一体化设计制造的有效手段。