Dynamic neighborhood genetic learning particle swarm optimization for high-power-density electric propulsion motor

To maximize the power density of the electric propulsion motor in aerospace application, this paper proposes a novel Dynamic Neighborhood Genetic Learning Particle Swarm Optimization (DNGL-PSO) for the motor design, which can deal with the insufficient population diversity and non-global optimal solution issues. The DNGL-PSO framework is composed of the dynamic neighborhood module and the particle update module. To improve the population diversity, the dynamic neighborhood strategy is first proposed, which combines the local neighborhood exemplar generation mechanism and the shuffling mechanism. The local neighborhood exemplar generation mechanism enlarges the search range of the algorithm in the solution space, thus obtaining high-quality exemplars. Meanwhile, when the global optimal solution cannot update its fitness value, the shuffling mechanism module is triggered to dynamically change the local neighborhood members. The roulette wheel selection operator is introduced into the shuffling mechanism to ensure that particles with larger fitness value are selected with a higher probability and remain in the local neighborhood. Then, the global learning based particle update approach is proposed, which can achieve a good balance between the expansion of the search range in the early stage and the acceleration of local convergence in the later stage. Finally, the optimization design of the electric propulsion motor is conducted to verify the effectiveness of the proposed DNGL-PSO. The simulation results show that the proposed DNGL-PSO has excellent adaptability, optimization efficiency and global optimization capability, while the optimized electric propulsion motor has a high power density of 5.207 kW/kg with the efficiency of 96.12%.     

Optimization of fixed aviation support resource station configuration for aircraft carrier based on aircraft dispatch mission scheduling

Before the dispatch of the carrier-based aircraft, a series of pre-flight preparation operations need to be completed on the flight deck. Flight deck fixed aviation support resource station configuration has an important impact on operation efficiency and sortie rate. However, the resource station configuration is determined during the aircraft carrier design phase and is rarely modified as required, which may not be suitable for some pre-flight preparation missions. In order to solve the above defects, the joint optimization of flight deck resource station configuration and aircraft carrier pre-flight preparation scheduling is studied in this paper, which is formulated as a two-tier optimization decision-making framework. An improved variable neighborhood search algorithm with four original neighborhood structures is presented. Dispatch mission experiment and algorithm performance comparison experiment are carried out in the computational experiment section. The correlation between the pre-flight preparation time (makespan) and flight deck cabin occupancy percentage is given, and advantages of the proposed algorithm in solving the mathematical model are verified.     

反舰导弹8字形二次攻击目标搜索效能评估模型

针对反舰导弹在一次攻击时未搜索到目标或搜索到目标后受电子干扰等因素,导致丢失目标而失去攻击机会的问题,通过分析反舰导弹的目标搜索过程,从充分利用剩余航程的角度出发,探讨了8字形二次攻击目标搜索的问题,研究了采取该方式搜索的效能影响因素,构建了8字形二次攻击搜索效能评估模型。通过仿真计算验证,论证了该模型的准确性,对研究反舰导弹二次攻击相关问题有一定的参考价值。     

探测点序列的最优化与简化计算方法

在搜索状态建模和求解一阶搜索状态方程的特征迹线解的基础上,建立了对随机运动进行离散时间探测的发现概率最优控制模型,结合动态规划原理给出一种最优探测点序列的逼近算法,并给出了短时计算的算法简化形式。在满足一阶搜索状态方程的随机恒速目标条件以及有限指数探测函数条件下,将给出的算法及其简化形式应用到算例。算例表明,当随机恒速运动目标初始位置和速度均服从圆正态分布时,该算法及其简化形式均能够由任意给定的初始探测点序列优化收敛到满足精度要求的最优探测点序列。     

大规模场景的百分比近邻软阴影绘制算法

针对传统软阴影绘制算法在面对大规模三维场景时绘制效率较低的问题,文章基于四叉树加速结构的阴影图构建,研究了邻域节点的搜索方法和遍历顺序的选择方法,提出了一种基于四叉树的百分比近邻软阴影算法。实验结果表明,与传统算法相比,该方法能够有效提高大规模三维场景的软阴影绘制效率,且性能稳定,可以渲染出高质量的软阴影效果。     

吊放声纳应召搜潜效能的建模与仿真

基于反潜直升机吊放声纳的探测原理及战术使用特点,在目标初始位置概略已知,速度和航向未知的条件下,分析了目标散布规律,建立了搜潜效能分析计算模型,给出了吊放声纳在扩展方形搜索方式下的应召搜潜概率；并采用蒙特卡洛法的基本思想对该计算模型进行了仿真验证,证明了该方法的有效性；最后,基于扩展方形搜索方式,研究了在单机和双机搜潜条件下搜索概率的不同结果,分析了潜艇速度、应召延迟时间、吊放次数等参数 对搜索概率的影响。     

基于冲突分类与消解的多智能体路径规划算法设计CSCD

多智能体路径规划应用广泛但求解困难。为更好地处理多智能体路径规划中的路径冲突问题,提高求解效率,将冲突进一步分类为相向顶点冲突和交叉顶点冲突,并提出了对应的消解方式。相向顶点冲突的消解方法采用提前添加约束的方式,避免在消解其冲突的过程中产生另一个可预见的冲突;交叉顶点冲突的消解方法采用寻找最佳等待时间的方式,在消解其冲突的同时消解其他存在的冲突。两种冲突消解方法均可减小约束树的规模,在一定程度上减少算法的计算量。并提出了基于冲突搜索算法的高层节点冲突搜索算法。实验结果表明,所提出的冲突分类及消解方式有效地减小了算法高层中约束树的规模,降低了算法计算量,并在智能体密集的环境下表现出更大的优势。     

未知动态环境下有人/无人机协同搜救研究

针对未知动态环境下搜救效率和智能化水平不高的问题，提出了一种基于变步长模型预测控制的有人机/无人机协同搜救方式。根据环境的动态变化性和探测的不确定性，建立了表示环境确定度和目标探测概率的动态搜救图模型，根据态势信息选择时间域步长，并根据有人机/无人机协同搜救任务特点，设计了搜救收益指标，并利用粒子群优化进行求解。仿真结果表明了本文方法的有效性和优越性。     

一种多目标变邻域模拟退火算法及成像星座任务规划方法

为提高成像星座观测任务的完成度和成像质量，提出一种多目标变邻域模拟退火算法(MOVNSA)。首先，分析了敏捷成像卫星观测任务约束和星上资源约束，构造了评价任务完成度和成像质量的目标函数，建立成像任务约束满足模型。然后通过设计多目标模拟退火算法中解的编码解码规则、变邻域搜索方法以及选拔淘汰机制，实现了观测任务成像时刻的滑动优化，兼顾提升了观测任务的完成度和成像质量。最后通过工程实例对多种算法进行测试和分析，结果表明该方法收敛速度快，优化效果好，可以在满足用户需求的前提下最大化任务的完成度。     

基于混合算法的涡轴发动机稳态性能仿真模型

以自由涡轮式涡轴发动机为研究对象,建立了涡轴发动机的稳态性能仿真模型,提出了基于天牛须算法和N+1点残量法的求解发动机模型的混合算法(BAS-N+1混合算法),利用发动机台架试车数据对仿真计算结果进行了验证。结果表明,该稳态性能仿真模型各参数的求解误差在3%以内。与PSO-N+1混合算法相比,BASN+1混合算法求解精度更高,收敛更快。BAS-N+1混合算法既保留了智能算法对初猜值误差的包容性,也拥有接近经典迭代算法的收敛速度和精度,能够实现涡轴发动机稳态仿真模型的高精度大范围快速收敛。