基于分解策略的SSO发射轨道遗传全局优化设计

提出了基于轨道分解优化和遗传算法（GA）的SSO发射轨道优化设计策略。针对多个轨道段相互耦合问题,基于分解优化策略,将整个发射轨道设计问题分解为两个轨道段设计问题。为了高效可靠地获得全局最优解,对基本遗传算法进行了改进。首先提出了基于多变异操作等改进措施的改进遗传算法;此外,结合遗传算法的全局搜索特性和Powell算法的局部搜索特性,设计了一种串行混合遗传算法。一个二级SSO运载火箭的计算结果表明,轨道分解优化策略确保了问题的成功求解,改进遗传算法和混合遗传算法均可稳定地获得全局最优解,但是混合算法更有效地提高了GA性能。     

A multi-objective multi-memetic algorithm for network-wide conflict-free 4D flight trajectories planning

Under the demand of strategic air traffic flow management and the concept of trajectory based operations (TBO),the network-wide 4D flight trajectories planning (N4DFTP) problem has been investigated with the purpose of safely and efficiently allocating 4D trajectories (4DTs) (3D position and time) for all the flights in the whole airway network.Considering that the introduction of large-scale 4DTs inevitably increases the problem complexity,an efficient model for strategic level conflict management is developed in this paper.Specifically,a bi-objective N4DFTP problem that aims to minimize both potential conflicts and the trajectory cost is formulated.In consideration of the large-scale,high-complexity,and multi-objective characteristics of the N4DFTP problem,a multi-objective multi-memetic algorithm (MOMMA) that incorporates an evolutionary global search framework together with three problem-specific local search operators is implemented.It is capable of rapidly and effectively allocating 4DTs via rerouting,target time controlling,and flight level changing.Additionally,to balance the ability of exploitation and exploration of the algorithm,a special hybridization scheme is adopted for the integration of local and global search.Empirical studies using real air traffic data in China with different network complexities show that the pro posed MOMMA is effective to solve the N4DFTP problem.The solutions achieved are competitive for elaborate decision support under a TBO environment.     

基于一种免疫遗传算法的自由飞行冲突解脱

自由飞行是解决航线拥挤的有效方法,但同时增加了空中交通管理的难度.免疫遗传算法是根据生物免疫原理提出的改进遗传算法,该算法主要体现了生物免疫机制中的免疫操作,与遗传算法相比,能较好地防治"早熟"和减少参数选取对结果的影响,优化效率高.将一种免疫遗传算法应用于自由飞行的冲突解脱问题,与标准遗传算法进行比较,取得了较好的优化效果.     

恶劣天气条件下航路网络修复优化

针对恶劣天气条件下可用空域资源不足导致的航班大面积延误问题,基于复杂网络修复理论和交通流分配理论,借鉴交通网络设计思想提出了一种航路网络修复优化策略。首先,建立了航路网络修复场景,基于气象信息生成了恶劣天气飞行受限区。然后,建立了上层模型以修复成本最低为目标函数、下层模型为多约束交通流分配模型的双层规划修复模型,应用改进粒子群算法对模型整体进行求解,结合K最短路径算法对下层模型进行求解。最后,提出局部和全局两类指标对航路网络修复效果进行评估。基于典型航路网络,以两类基础修复策略为对比方法,同时对比了实际运行结果,研究了不同修复策略的修复效果和适用性。仿真结果表明：航路网络修复优化策略既能弥补原有拓扑结构修复策略的结构受限不足,又能解决拓扑结构调整修复策略带来的巨额协调费用问题,能够保证在对正常运行航班干扰最小的同时,以最小的修复成本使所有受影响的航班都恢复正常运行,对于减缓航路拥堵和航班延误有极大的意义。     

结合跳点引导的无人机随机搜索避撞决策方法

针对无人机在城市空域环境和密集交通流下的避撞决策问题，提出马尔科夫决策过程（MDP）和蒙特卡洛树搜索（MCTS）算法对该问题进行建模求解。蒙特卡洛树搜索算法在求解过程中为保证实时性而使其搜索深度受限，容易陷入局部最优，导致在含有静态障碍的场景中无法实现避撞的同时保证全局航迹最优。因此结合跳点搜索算法在全局规划上的优势，建立离散路径点引导无人机并改进奖励函数来权衡飞行路线，在进行动态避撞的同时实现对静态障碍的全局避撞。经过多个实验场景仿真，其结果表明改进后的算法均能在不同场景中获得更好的性能表现。特别是在凹形限飞区空域仿真模型中，改进后的算法相对于原始的蒙特卡洛树搜索算法，其冲突概率降低了36%并且飞行时间缩短47.8%。     

基于代理模型方法的翼型优化设计

提出了基于代理模型的两步优化方法,用于翼型在黏性流场中气动外形的优化设计.第一步优化使用基于代理模型的遗传算法(GA)获得全局最优解的大致范围.以本征正交分解(POD)方法作为第一步优化中气动力计算的代理模型方法,降低遗传算法的计算量,并对其采样解的生成方法进行改进,提高了计算精度;第二步优化使用基于NavierSto...     

Strategic flight assignment approach based on multi-objective parallel evolution algorithm with dynamic migration interval

The continuous growth of air traffic has led to acute airspace congestion and severe delays, which threatens operation safety and cause enormous economic loss. Flight assignment is an economical and effective strategic plan to reduce the flight delay and airspace congestion by reasonably regulating the air traffic flow of China. However, it is a large-scale combinatorial optimization problem which is difficult to solve. In order to improve the quality of solutions, an effective multi-objective parallel evolution algorithm(MPEA) framework with dynamic migration interval strategy is presented in this work. Firstly, multiple evolution populations are constructed to solve the problem simultaneously to enhance the optimization capability. Then a new strategy is proposed to dynamically change the migration interval among different evolution populations to improve the efficiency of the cooperation of populations. Finally, the cooperative co-evolution(CC) algorithm combined with non-dominated sorting genetic algorithm II(NSGA-II) is introduced for each population. Empirical studies using the real air traffic data of the Chinese air route network and daily flight plans show that our method outperforms the existing approaches, multiobjective genetic algorithm(MOGA), multi-objective evolutionary algorithm based on decomposition(MOEA/D), CC-based multi-objective algorithm(CCMA) as well as other two MPEAs with different migration interval strategies.     

基于遗传算法的复杂多峰函数优化

从传统的搜索、优化方法入手，分析了遗传算法对复杂、多峰函数优化的有效性；简单介绍了遗传算法；并归纳出从优化问题转化到用遗传算法求解的转化思路；最后，通过对一个多峰函数优化的遗传算法求解，验证了此方法在全局优化、鲁棒性等方面的更有效性。     

声纳浮标阵目标搜索优化布放算法

为提高声纳浮标阵的目标搜索效率,文章首先建立目标运动模型和累积搜索概率的计算方法,然后提出了基于多点随机搜索、分区分支界定和遗传算法的浮标阵优化布放方法。仿真结果表明：这3种优化方法均优于传统布放方法,多点随机搜索算法性能最差但适合短时间制定布放策略;分区分支界定算法适合优化较少参数的浮标阵形,保证能找到最优点;对于较...     

Gaussian fitting based optimal design of aircraft mission success space using multi-objective genetic algorithm

In order to obtain the optimized aircraft design concept which meets the increasingly complex operation environment at the conceptual design stage, System-of-systems (SoS) engineering must be considered. This paper proposes a novel optimization method for the design of aircraft Mission Success Space (MSS) based on Gaussian fitting and Genetic Algorithm (GA) in the SoS area. First, the concepts in the design and evaluation of MSS are summarized to introduce the Contribution to System-of-Systems (CSS) by using a conventional effectiveness index, Mission Success Rate (MSR). Then, the mathematic modelling of Gaussian fitting technique is noted as the basis of the optimization work. After that, the proposed optimal MSS design is illustrated by the multi-objective optimization process where GA acts as the search tool to find the best solution (via Pareto front). In the case study, a simulation system of penetration mission was built. The simulation results are collected and then processed by two MSS design schemes (contour and neural network) giving the initial variable space to GA optimization. Based on that, the proposed optimization method is implemented under both schemes whose optimal solutions are compared to obtain the final best design in the case study.     

基于粒子群和人工蜂群混合算法的气动优化设计

现代启发式智能算法存在全局与局部搜索能力的平衡问题,针对此问题,采用双种群进化策略和信息交流机制,提出一种基于粒子群算法和人工蜂群算法相结合的新型混合优化算法——MABCPSO,并分别进行函数测试和翼型的气动优化设计验证。结果表明:MABCPSO新型混合优化算法具有更好的寻优能力,相比粒子群算法和人工蜂群算法,该算法能以更少的进化代数分别提高1.7%和2.2%的减阻效果。     

考虑轴承游隙的非线性动力学轴承-转子系统优化

针对一类带动态性能约束的轴承-转子系统优化设计存在的问题,做如下改进工作:一是在目前一类轴承-转子系统优化模型基础上,将轴承游隙作为设计变量,考虑轴承游隙的影响;二是提出了一种演化算法/线性搜索的混合算法,将优化过程分为两个阶段,首先采用演化算法对问题全局寻优,求得给定代数时的优化解,再以此解作为新的初始解,采用线性搜索方法进行局部搜索.经数值仿真表明:该优化模型中增加考虑轴承游隙后,对优化结果有较大影响;提出的混合算法克服了使用线性搜索方法难以确定初始解的问题,在同等计算精度和耗时情况下,该方法求解成功率较高.      

航路网络生成及优化

空中交通流运行的安全性和成本取决于航路网络结构。基于节点度实现航路网络枢纽节点和干线节点分层,利用全局耦合生成主干网络,运用距离连接概率实现干线节点社区化,采用连接概率生成社区网络;采用DBSCAN聚类算法,基于角度和距离实现航路网络优化;以中国大陆空域为例,实现了主干航路网络和局部干线航路网络的生成及优化,验证了方法的可行性。在与相关研究对比,基于节点度的枢纽节点选取更加客观合理,所构建的分层航路网络结构符合机场点的分布特性,采用DBSCAN聚类算法优化效率明显提高。     

一种自适应随机优化算法

提出了一种新的基于群体搜索的随机优化算法。该算法的基本思想是：１） 像遗传算法那样,采用二进制串来表示问题的尝试解;２） 解串中每个二进制位的取值,由一可改变的概率分布控制;３）根据群体中最佳解串与最差解串编码结构上的差异,对相关的概率分布进行修改。文中给出的模拟实例,显示了新算法在求解函数全局优化问题中的有效性     

基于混合规划策略的自由漂浮冗余机械臂减振运动规划(英文)

对自由漂浮柔性冗余机械臂系统的减振运动规划问题进行研究。首先,采用拉格朗日方法建立系统的动力学模型,并给出了减振运动规划的模型。其次,提出一种基于高斯伪谱法与直接打靶法的混合规划策略,用于求解运动规划问题。先利用高斯伪谱法将运动规划问题转化为一个非线性参数优化问题,并用遗传算法确定近似解,随后利用直接打靶法建立优化模型,将得到的近似解作为优化变量的初值,并采用序列二次规划算法求得精确解。最后,对算例进行分析,验证了基于混合规划策略得到的关节运动轨迹能够明显减小柔性臂杆的全局振动以及残余振动。数值计算的结果表明该方法对减振运动规划问题的求解是有效且稳定的。     

基于改进引力搜索算法的认知无线网络频谱分配

为了提高认知无线网络频谱资源利用率,提出一种基于改进引力搜索算法的频谱分配方案。该方案 将质子位置信息映射为频谱分配变量,提出一种非线性引力衰减因子,以增强算法对于频谱分配可行解的搜索 能力。其次,为了保持算法对于频谱分配可行解的最优搜索方向,采用全局最优引导策略为质子增加记忆能力。 然后,提出动态高斯扰动策略,随着算法迭代自适应地调整扰动步长,避免算法陷入局部最优。最后,针对频 谱分配问题的离散化特性,引入Sigmoid 函数使质子的位置离散化。仿真实验结果表明,改进引力搜索算法能 够有效地实现频谱分配问题的优化求解,取得了相对较高的网络效益和用户公平度。     

A quantum inspired genetic algorithm for multimodal optimization of wind disturbance alleviation flight control system

This paper develops a Quantum-inspired Genetic Algorithm (QGA) to find the sets of optimal parameters for the wind disturbance alleviation Flight Control System (FCS). To search the problem domain more evenly and uniformly, the lattice rule based stratification method is used to create new chromosomes. The chromosomes are coded and updated according to quantum-inspired strategies. A niching method is used to ensure every chromosome can converge to its corresponding local minimum in the optimization process. A parallel archive system is adopted to monitor the chromosomes on-line and save all potential feasible solutions in the optimization process. An adaptive search strategy is used to gradually adjust the search domain of each niche to finally approach the local minima. The solutions found by the QGA are compared with some other Multimodal Optimization (MO) algorithms and are tested on the FCS of the Boeing 747 to demonstrate the effectiveness of the proposed algorithm.     

基于改进多目标布谷鸟搜索算法的翼型气动优化设计

布谷鸟搜索（CS）算法是一种新型的受自然现象启发的元启发式智能优化算法，其强大的全局搜索能力和收敛速度受到了广泛关注。多目标布谷鸟搜索（MOCS）算法是一种在单目标布谷鸟算法基础上发展的可以直接求解Pareto解集的多目标优化算法。针对原始MOCS算法的不足，采用一系列措施以提高算法的收敛精度、收敛速度以及解的均匀性：通过引入非支配排序与拥挤距离来改进解的适应度评估；通过改进随机游走策略来提高局部搜索能力；通过引入改进的自适应丢弃概率策略来提高算法的收敛速度；加入档案管理机制，提高解的均匀性。典型的多目标数值算例结果表明，改进的MOCS算法相较于当前主流的NSGA-Ⅱ算法拥有更快的收敛速度和更高的收敛精度。以RAE2822双目标升阻比优化设计为例，将改进的MOCS算法应用于多目标气动优化中，改进的MOCS算法共获得64个Pareto解，优化后的翼型气动性能有明显的提升，设计者可以根据自己的偏好选取不同的Pareto解。对于气动优化问题，改进的MOCS算法与目前主流的NSGA-Ⅱ相比，收敛速度更快。     

Identifying vital edges in Chinese air route network via memetic algorithm

Due to rapid development in the past decade, air transportation system has attracted considerable research attention from diverse communities. While most of the previous studies focused on airline networks, here we systematically explore the robustness of the Chinese air route network, and identify the vital edges which form the backbone of Chinese air transportation system. Specifically, we employ a memetic algorithm to minimize the network robustness after removing certain edges, and hence the solution of this model is the set of vital edges. Counterintuitively, our results show that the most vital edges are not necessarily the edges of the highest topological importance, for which we provide an extensive explanation from the microscope view. Our findings also offer new insights to understanding and optimizing other real-world network systems.     

混合生物生长自适应搜索遗传算法在形状优化中的应用

利用自适应搜索遗传算法和生物生长算法的特点,提出一种新的优化方法—混合生物生长自适应搜索遗传算法。该算法即可充分利用前两种算法的优点,又可弥补二者的不足。为了验证该算法的合理性和正确性,对经典算例三杆桁架结构进行了优化,并将新算法进一步应用于具有复杂结构的三维离心叶轮优化设计中,结果表明混合算法较遗传算法收敛速度快,且可得到形状优化最优解。