Optimizing Search Plans for Teams of Mobile and Stationary Searchers With a New Class of Searchers Over a Multi-Zoned Domain and Finite Time



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This dissertation introduces a new search theory methodology, nicknamed Pathfinder, that can optimize teams of heterogeneous mobile and stationary searchers as well as searchers that can transport other searchers. In addition, Pathfinder can optimize searches over a multi-zone domain and model target behavior based on environmental and behavioral factors. Pathfinder accomplishes this by using an agent-based model to estimate target movement; then it uses nonlinear optimization, teamed with a genetic algorithm, to find optimal search plans. The optimization model ensures that search plans should be easy to both implement and create in reality, in addition to having a high probability of finding a target. Thus, the optimization model includes movement constraints that make search plans easier to implement and more cost effective. The obtained results from numerous simulations demonstrate that the new methodology has the potential to advance current search theory as well as to enhance current maritime search operations.



Agent-Based Modeling, Optimization, Search and Rescue, Search Planning, Search Theory, Simulation