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基于改进蚁群算法航空电缆路径规划

杨禹成,  卢洪义,  章斌,  桑豆豆,  刘舜

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文章导航 > 航空动力学报  > 2023 >  38(7): 1715-1722
杨禹成, 卢洪义, 章斌, 等. 基于改进蚁群算法航空电缆路径规划[J]. 航空动力学报, 2023, 38（7）：1715-1722 doi: 10.13224/j.cnki.jasp.20220708
引用本文: 杨禹成, 卢洪义, 章斌, 等. 基于改进蚁群算法航空电缆路径规划[J]. 航空动力学报, 2023, 38（7）：1715-1722 doi: 10.13224/j.cnki.jasp.20220708
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YANG Yucheng, LU Hongyi, ZHANG Bin, et al. Route planning of aviation cable based on improved ant colony algorithm[J]. Journal of Aerospace Power, 2023, 38（7）：1715-1722 doi: 10.13224/j.cnki.jasp.20220708
Citation: YANG Yucheng, LU Hongyi, ZHANG Bin, et al. Route planning of aviation cable based on improved ant colony algorithm[J]. Journal of Aerospace Power, 2023, 38（7）：1715-1722 doi: 10.13224/j.cnki.jasp.20220708
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基于改进蚁群算法航空电缆路径规划

doi: 10.13224/j.cnki.jasp.20220708

  • 南昌航空大学 飞行器工程学院，南昌 330063

基金项目: 江西重点基金（20201BBE51002）； 南昌航空大学研究生创新专项基金（YC2021-060）
详细信息
    作者简介:

    杨禹成（1998-），男，硕士生，主要研究方向为航空发动机测控系统设计

  • 中图分类号: V263.2；TP391.9

Route planning of aviation cable based on improved ant colony algorithm

  • School of Aircraft Engineering，Nanchang Hangkong University，Nanchang 330063，China

    摘要
  • 摘要:

    针对航空电缆在布局空间安装中存在的可靠性差，效率低和成本高等问题，提出了一种基于改进蚁群算法的航空电缆布局路径规划优化方法。对布线安装空间进行栅格化处理，通过分析航空布线要求与约束条件，对待布线安装空间进行模拟真实环境建模，获得的建模空间用于航空电缆的二维布线路径优化。采用了向终点方向引导的转移规则，并增加转弯拐角惩罚因子，来改进启发函数，减少了路径搜索的盲目性，提高了规划路径平滑度；采用一种自适应调整方式的信息素挥发因子，提高算法的搜索效率和后期收敛速度；引入了遗传变异，避免算法陷入局部最优。在仿真实验中，将所提出的方法与其他算法进行了对比分析并表明：应用该算法优化后总体电缆的路径布局电缆路径明显减少、即电缆长度用量减少；拐点数明显减少、即电缆电器性能变好，能够提供航空发动机系统的稳定性。验证了该算法的可行性和有效性。

     

    Abstract:

    In view of the problems of poor reliability, low efficiency and high cost in planning space installation of aviation cable, an optimized method of route planning of aviation cable layout based on improved ant colony algorithm was proposed. The wiring installation space was rasterized, and the real environment modeling was carried out for the wiring installation space by analyzing the requirements and constraints of aviation wiring. The modeling space obtained was used to optimize the two-dimensional routing path of aviation cables. The heuristic function was improved by using the transition rule guided to the destination direction and increasing the penalty factor of turning corner, which reduced the blindness of path search and improved the smoothness of the planned path. An adaptive adjustment of pheromone volatile factor was used to improve the search efficiency and convergence speed of the algorithm. Genetic variation was introduced to avoid the algorithm falling into local optimum. In the simulation experiment, compared with other algorithms, the proposed method can significantly reduce overall cable path layout and the number of inflection points, indicating that the cable length was reduced and the cable electrical performance was better, enabling to provide the stability of the aircraft engine system. The feasibility and effectiveness of the proposed algorithm were verified.

     

    • HTML

  • 图 1  航空发动机实物布线图

    Figure 1.  Aero-engine physical wiring diagram

    下载: 全尺寸图片 幻灯片

    图 2  电缆布局约束分析

    Figure 2.  Cable layout constraint analysis

    下载: 全尺寸图片 幻灯片

    图 3  静电感应等效电路

    Figure 3.  Electrostatic induction equivalent circuit

    下载: 全尺寸图片 幻灯片

    图 4  电磁感应等效电路

    Figure 4.  Electromagnetic induction equivalent circuit

    下载: 全尺寸图片 幻灯片

    图 5  WS10发动机实物图

    Figure 5.  Actual diagram of the WS10 engine

    下载: 全尺寸图片 幻灯片

    图 6  环境模型

    Figure 6.  Environment model

    下载: 全尺寸图片 幻灯片

    图 7  改进蚁群算法实现步骤流程

    Figure 7.  Improved ant colony algorithm implementation step flow

    下载: 全尺寸图片 幻灯片

    图 8  经典遗传算法路径规划图

    Figure 8.  Classic genetic algorithm route planning diagram

    下载: 全尺寸图片 幻灯片

    图 9  经典蚁群算法路径规划图

    Figure 9.  Classical ant colony algorithm route planning diagram

    下载: 全尺寸图片 幻灯片

    图 10  文献[18]算法路径规划图

    Figure 10.  Reference [18] algorithm route planning diagram

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    图 11  本文改进蚁群算法路径规划图

    Figure 11.  The route planning diagram of the improved ant colony algorithm in this paper

    下载: 全尺寸图片 幻灯片

    图 12  迭代收敛图

    Figure 12.  Iterative convergence graph

    下载: 全尺寸图片 幻灯片

    表  1  参数设置

    Table  1.   Parameter settings

    参数数值
    起点0
    终点399
    蚂蚁个数M50
    最大迭代次数K100
    信息素因子α1
    启发函数因子β7
    信息素挥发因子最小值ρ0.3
    信息素增加强度系数Q1
    遗传变异概率Pb0.04
    下载: 导出CSV

    表  2  4种算法10次对比结果

    Table  2.   10 comparison results of 4 algorithms

    算法平均最短
    路径长度    		平均
    拐点数    		平均
    迭代次数
    经典遗传算法35.74362242
    文献[18]改进算法31.47481217
    经典蚁群算法35.88431772
    改进蚁群算法30.3848611
    下载: 导出CSV
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  • 收稿日期:  2022-09-20
  • 网络出版日期:  2023-06-12

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