Air combat tactics optimization using stochastic genetic algorithms

Authors:
  • KHK. Harper
    K. Harper
  • KKK. Krishnakumar
    K. Krishnakumar
  • GZG. Zacharias
    G. Zacharias
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Abstract
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.

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