基于改进细菌觅食算法的飞控系统多模态参数优化
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作者单位:

长安大学 汽车学院

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中图分类号:

TP273

基金项目:

国家重点研发计划资助项目(2019YFB1600800); 中国博士后科学基金资助项目(2019M660246); 陕西省自然科学基础研究计划资助项目(2021JQ-287, 2021JQ-252); 长安大学中央高校基本科研业务费专项资金资助项目(300102220304)


An improved bacterial foraging algorithm for multimodal parameter optimization of the flight control system
Author:
Affiliation:

School of Automobile, Chang''an University

Fund Project:

National Key Research and Development Program of China(2019YFB1600800); Project funded by China Postdoctoral Science Foundation(2019M660246); Natural Science Basic Research Program of Shaanxi (2021JQ-287, 2021JQ-252); Fundamental Research Funds for the Central Universities, CHD (300102220304)

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    摘要:

    针对飞控系统参数优化过程中存在的解空间非凸性问题,或由于多约束条件下导致的全局最优不可达问题,提出了一种基于改进细菌觅食算法的多模态参数优化方法。通过采用基于格型准则的采样方法以尽可能广泛地搜索解空间,并利用K均值聚类的小生境技术,使得多个细菌种群能够分别搜索各自的区域以尽可能多地获得解空间中不同位置的可行解。同时研究一种自适应深度搜索策略,确保算法在整个寻优过程中的鲁棒性。所提算法可以在完成对系统优化的基础上,探寻飞控系统中各参数本身的可行域及其在解空间中所处的位置,同时也能够在一定程度上揭示解空间本身的特性。最终仿真结果验证了所提算法可以有效地简化系统调参的过程,更为快速地获得一个满足设计性能期望的飞控系统。

    Abstract:

    A multimodal parameter optimization method based on an Improved Bacterial Foraging Optimization (IBFO) algorithm is proposed to deal with the non-convexity problem in the solution space or the global optimal unreachable problem caused by multiple constraints in the process of parameter optimization of flight control system. The sampling method based on lattice criterion is used to search the solution space as widely as possible. The niche technology based on K-means clustering is used to make multiple populations search their own space respectively and obtain as many feasible solutions as possible in different regions of the solution space. At the same time, the adaptive depth search strategy is used to ensure the robustness of the algorithm in the whole optimization process. The proposed algorithm can explore the feasible region of each solution in the flight control system and the relationship between them. Also, the proposed algorithm can reveal the characteristics of the solution space to a certain extent. Finally, the simulation results show that the proposed algorithm can effectively simplify the process of system parameter tuning, and obtain a flight control system that meets the design performance expectations more conveniently.

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历史
  • 收稿日期:2021-01-22
  • 最后修改日期:2021-05-20
  • 录用日期:2021-06-03
  • 在线发布日期: 2021-07-01
  • 出版日期: