中国科学院自动化研究所机构知识库

本文首先对透明态势下无人机集群近视距空战协同任务目标分配这一问题进行了数学建模。针对在建模后问题求解过程中时常产生的“算法易陷入局部极值而无法继续搜索”这一“算法早熟”问题，在混合粒子群算法（HPSO）基础上，提出了一种引入堵塞检测机制和强制打散操作的自打散混合粒子群算法（A-HPSO）。为验证本文研究思路及改进后算法性能，分别将HPSO算法及其两种改进型算法M-HPSO算法和I-HPSO算法与A-HPSO算法在六种标准测试目标优化函数及固定-随机两种空战态势下所建模问题上进行对比仿真实验。实验结果显示，在多约束多目标函数的优化求解上，改进后的A-HPSO算法显著避免了“算法早熟”问题的发生，并进一步提升了HPSO算法的收敛速度和收敛精度。

In this paper, firstly, the theoretical problem of target assignment in close range air combat cooperative task of UAV cluster under transparent situation is modeled mathematically. Aiming at the problem of "premature algorithm", which often occurs in the process of problem solving after modeling, that "the algorithm is easy to fall into local extremum and cannot continue to search", a self breaking hybrid particle swarm optimization (A-HPSO) algorithm is proposed based on the previous hybrid particle swarm optimization (HPSO) algorithm, which introduces congestion detection mechanism and forced dispersion operation. In order to verify the research idea of this paper and the performance of the improved algorithm, HPSO, M-HPSO, I-HPSO and A-HPSO algorithms are respectively applied to the simulation experiments of six standard test target optimization functions and the simulation experiments of modeling problems under two kinds of fixed-random air combat situations. The experimental results show that compared with the original algorithm, the improved algorithm A-HPSO significantly overcomes the "premature algorithm" problem and further improves the convergence speed and accuracy of the HPSO algorithm in the optimization of multi-constraint and multi-objective functions.