逆转变异蚁群算法在CGF多目标分配中的应用

在预警机指挥引导多机协同空战对抗仿真过程中,为了提高CGF实体的智能性和实时性,对多CGF实体协同作战时目标选择因素进行分析,构建了一种基于变异蚁群算法的多CGF实体协同作战目标选择模型。该模型对蚁群算法中的选择策略进行了改进,引入一种遗传算法的变异算子以减少最优解的搜索时间,改进了搜索空间中信息素的更新方式,提高了模型最优解的搜索能力。运用该模型对多CGF实体协同作战过程进行仿真,仿真结果表明,所提出的变异蚁群算法对多CGF实体目标选择最优解的搜索效率明显优于基本蚁群算法,能够更好地模拟真实作战兵力的目标选择过程。

Application of Reverse Aberrance Ant Colony Algorithm for CGF Multi-Target Assignment

In simulation of multi-aircraft coordinated air combat under command and control of early warning aircraft,the CGF Cooperative Multi-Target Selection Optimization (CMTSO) factors were analyzed,and a Reverse Aberrance Ant Colony Algorithm (RAACA) model was built up to solve the Multiple Targets Selection (MTS) problem for the cooperative CGF entities in order to improve its intellectuality and real-time performance.The selection strategy of Ant Colony Algorithm was modified,and the reverse aberrance operator of the genetic algorithm was embedded to reduce the search time.In addition,the pheromone updating mode was modified during searching the solution to improve the search performance.The simulation results for the CMTSO show that the search efficiency of the proposed algorithm is obviously superior to that of the basic Ant Colony Algorithm (ACA),and the algorithm can realistically simulate the MTS process of real battle forces.