Research On Evaluation And Optimization Of Combat Network Based On OODA Loop

In modern warfare,the various combat entity units and their combat relationships distributed in the battlefield space constitute the combat network.This paper focuses on the construction of combat network,the identification of OODA loops and motifs in combat network,the evaluation of the importance of combat network nodes and OODA loops,and the optimization of combat network resource allocation.For the issue of combat network construction,the node capability attribute model of combat network and the connection rules of nodes are proposed,and the combat network model is constructed by combining rule reasoning method.Based on the combat network model,the VF2 algorithm is applied to the search of OODA loops and motifs in the combat network.The three types of feasibility rules of the basic VF2 algorithm are combined with the connection rules of the combat network nodes,and the search tree is pruned in advance during the search process,which can optimize algorithm’s search space,and can quickly and efficiently search for OODA loops and motifs in the combat network.The comparison and analysis of the operational efficiency of several search algorithms through cases verify the effectiveness of the VF2 algorithm in the OODA loop search in the combat network.For the searched OODA loops and motifs.Firstly,the evaluation indicators of the nodes of the combat network and the OODA loop are proposed.Then determine the index weight by the coefficient of variation method.Finally,the importance of the nodes of the combat network and the OODA loops are ranked by the TOPSIS algorithm.For the optimization of combat network resource allocation,a resource allocation optimization model for combat effectiveness and a resource allocation optimization model for OODA loop combat capabilities are constructed respectively.Considering the influence of topology structure of OODA loop on combat effectiveness and the influence of uncertainty during combat on combat capability.The genetic algorithm is used to solve the two resource allocation optimization problems respectively.The effectiveness of the algorithm is verified through a case,and the combat effectiveness and the effectiveness of the algorithm is verified by a case.