Multi domain dynamic targeting: recommending solutions using optimized coordinated algorithms that adapt to the mission
Jinhong K. Guo, Jennifer Lautenschlager, Valerie Champagne, Jose F. Pascual-Amadeo, Phillip Warlick, Héctor J. Ortiz-Peña, Dustin Naylor, Benjamin Ritz, Tim Schuler, Kevin Costantini, Moises Sudit · 2022
To increase efficiency in all-domain military operations, such as achieving desired effects within severely compressed decision cycles, a multi-prong approach to technology is required. Traditional approaches provide a single algorithmic solution that keeps users, and acquisition efforts, “locked-in” to a result that may not be ideal for a particular mission, or possibly hinders technological advancements. Combining algorithms through ‘plug and play’, users can select the optimal algorithm(s) for their mission, and the acquisition community can easily improve existing or introduce new algorithms, thereby increasing performance while reducing cost. Our solution provides a set of heterogeneous independent optimization algorithms (IOAs) developed separately by three defense contractors, coordinated by a central Meta- Optimizer (MO) that is connected to a simulation and testing (S&T) environment.