Air Combat Tactics Optimization using Stochastic Genetic Algorithms

This paper describes the development of a software tool for optimizing large-scale air combat tactics using stochastic genetic algorithms. The tool integrates four key components: 1) autonomous blue/red player agents, with their individual aircraft and tactics; 2) an engagement simulator used to play out a tactical scenario; 3) performance metrics reflecting engagement outcome and tactical advantage; and 4) a GA “engine” for performance-based optimization of blue team tactics. The tool’s capabilities are demonstrated through the optimization of blue team formation and intercept geometry in a series of tactical engagements. The tactics implementation uses a hierarchical concept that builds large formation tactics from small conventional fighting units, facilitating the design of tactics compatible with existing air combat principles.