基于并行蚁群优化的多UCAV任务分配仿真平台  

Multi-UCAVs Task Assignment Simulation Platform Based on Parallel Ant Colony Optimization

作　　者：段海滨[1] 丁全心[2] 常俊杰[1] 刘森琪[1] 

机构地区：[1]北京航空航天大学自动化科学与电气工程学院 [2]中国一航洛阳电光设备研究所火力控制技术国防科技重点实验室,河南洛阳471009

出　　处：《航空学报》2008年第B05期192-197,共6页Acta Aeronautica et Astronautica Sinica

基　　金：国家自然科学基金(60604009);北京市科技新星计划(2007A017);航空科学基金(2006ZC51039);苏州大学江苏省计算机信息处理技术重点实验室开放基金

摘　　要：多无人作战飞机(UCAV)协同作战是UCAV参与战斗的主要模式,而多UCAV任务分配是多UCAV协同作战研究的关键问题。针对现有多UCAV任务分配方法中所存在的计算量大、运行时间长等问题,提出了一种基于并行蚁群优化(ACO)的多UCAV任务分配方法。在构建多UCAV空战优势矩阵的基础上,给出了综合态势评估函数;随后阐述了基本ACO算法的基本原理和数学模型,提出了一种用并行ACO算法解决多UCAV任务分配问题的实现方法;最后基于MATLAB图形用户界面(GUI)开发了一种基于并行蚁群优化的多UCAV任务分配仿真平台。实践证明该仿真平台具有良好、开放的可扩展性,且使用方便。Multiple unmanned combat aerial vehicles （UCAVs） coordinated combat is the main mode to put UCAVs into the battle, while multiple UCAVs task assignment is the key problem of multiple UCAVs coordinated combat. There have recently been some reports on approaches to multiple UCAVs task assignment. But these approaches have some disadvantages, such as being complex on computing, long computing time and so on. In order to overcome the shortages of these approaches, a novel method based on improved ant colony optimization （ACO） is proposed in this paper. Firstly, various air combat predominance functions are deduced, and the comprehensive situation assessment function is built. On the introduction of basic ACO principle and mathematical model, a parallel ACO model is pro- posed to solve the multiple UCAVs target assignment problem. Subsequently, the parallel ACO pseudo-codes are given in detail. Finally, a multiple UCAVs task assignment simulation platform based on parallel ACO is developed. This platform takes MATLAB graphical user interface （GUI） as the developing tool. Exact applications show that this simulation platform is efficient, easy to use and modify.

关 键 词：蚁群优化 无人作战飞机 态势评估 任务分配 

分 类 号：V271.4[航空宇航科学与技术—飞行器设计]