An Operation Planning Generation and Optimization Method for the New Intelligent Combat SoS

With the rapid development of emerging technologies, the unmanned and intelligent degree of combat forces in the combat system of systems(SoS) has been continuously improved, making the operational equipment more diverse. How to better describe the multiple functions and forces hybrid combat SoS becomes a difficult problem. Changes in combat forces have transformed the operational style from single platform to cluster, which puts forward higher demands on the timeliness of the development of course of action(COA), which is the key step in the formulation of operational planning. It is of great military value to study the model of the hybrid new complex combat SoS, as well as the generation and optimization of the operational plan under the new combat model. This paper extends the FINC (Force, Intelligence, Networking, C2) model of heterogeneous networks, adds a variety of new intelligent unmanned combat force nodes, and proposes a new Force Intelligent Network C2 and Autonomous Model (FINCA²) which represents more different types of function nodes and communication relationships between nodes. Based on this, this paper proposes six types of 12 kinds functionally simple, frequently used, and practically function chains (FC) for the new combat SoS. Through the Joint Mission Thread (JMT) method, the operational mission is decomposed to get specific operational actions, action relationships, and requirements list of function chains for the actions, and complete the matching of actions and resources. Based on the idea of sigmoid function in neural network, the multi-objective optimization algorithm NSGA-III is improved to find the Pareto frontier of the action order scheme that satisfies the task association relationship. Finally, the method proposed in this paper is verified by the case of a brigade-level combat SoS participating in border operations.