Multi Domain Dynamic Targeting: Recommending Solutions using Optimized Coordinated Algorithms that Adapt to the Mission

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. The MO relays S&T changes to the IOAs which independently optimize resourcing for any new dynamic targets (DTs) while minimizing ripple effects (e.g., changes to executing tasks) and maximizing the diversity of options across all domains. Each IOA proposes potentially multiple options which are presented to users for a tradeoff analysis using quantitative factors such as speed, allocation percentage, attribution, desired effects, and degree of ripple effects. A single option, selected by the users, is passed back to both the S&T environment and the connected IOAs, enabling the IOAs to learn from the operator’s choices. The heterogeneity of the independent optimizers provides more diversified and efficient solutions across all domains from which the user can select the best option for the specific mission they are trying to achieve.