Abstract
Aiming at the target allocation of multi-aircraft cooperative air combat, an improved artificial immune algorithm is proposed based on the modeling for comprehensive superiority function. Firstly, the random generation method and artificial construction method are used to create two initial populations, which ensure the diversity of the initial populations; following is to evolve the populations by adopting two different selection, crossover and mutation operations; then, the designed new immigration operator is used to exchange information among the populations, which further increases the diversity of the populations and improves the search efficiency. Finally, the comparison of the improved artificial immune algorithm with the traditional artificial immune algorithm had been made. Simulation results show that the improved artificial immune algorithm can effectively improve the premature convergence problem, and the search efficiency, the optimal allocation scheme is obtained, which is suitable for target allocation problem of multi-aircraft cooperative air combat and meet the actual operational requirements.
Highlights
人工免疫算法是模仿生物信息系统中的抗原识 别、记忆等功能而提出的一种智能计算方法。 多机 协同空战目标分配问题与人工免疫系统( AIS) 有很 多相似之处,如表 1 所示。
Aiming at the target allocation of multi⁃aircraft cooperative air combat, an improved artificial immune al⁃ gorithm is proposed based on the modeling for comprehensive superiority function
The random generation method and artificial construction method are used to create two initial populations, which ensure the diversity of the initial populations; following is to evolve the populations by adopting two different selection, crossover and mutation operations; the designed new immigration operator is used to exchange information among the populations, which further increases the diversity of the populations and improves the search efficiency
Summary
人工免疫算法是模仿生物信息系统中的抗原识 别、记忆等功能而提出的一种智能计算方法。 多机 协同空战目标分配问题与人工免疫系统( AIS) 有很 多相似之处,如表 1 所示。 少种群间信息之间的交流,搜索效率低,难以保持种 群多样性,容易陷入局部最优。 基于此,本文设计了 一种并行人工免疫算法,具体操作流程和算法流程 图如图 4 所示。 从表 4 可以看出,采用本文算法对文献[ 11] 中 的算例进行仿真,在采用迁徙比例 B 时,效果最好, 采用迁徙比例 C 时,效果其次,采用迁徙比例 A 时, 效果最差。 将表 4 分别与表 5、表 6 和表 7 进行对比 可得:本文算法在采用迁徙比例 B 时,与其他 3 种 算法相比具有比较明显的优势;本文算法在采用迁 徙比例 C 时,较传统算法和文献[11] 中算法有比较 明显的优势,较文献[14] 中算法,优势很小;本文算 法在采用迁徙比例 A 时,与传统算法相比较,有明 显的优势,与文献[11] 中的算法相比较,优势很小, 与文献[14] 中的算法相比较,具有一定的劣势。 进 一步说明了本文中改进人工免疫算法的性能受迁徙 比例的影响,在采用迁徙比例( 15%,12%,9%) 时, 算法效果最优。 采用并行运行方式并增加迁徙操 计算机应用研究, 2018, 35 ( 9) : 2597⁃2601 WANG Qinghe, WAN Gang, CHAI Zheng, et al Multiple Targets Assignment of Multiple UAVs′ Cooperation Based on Im⁃ proved Genetic Algorithm[ J] .
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