We model an intelligence collection activity as multiobjective optimization on a binary stochastic physical search problem, providing formal definitions of the problem space and nondominated solution sets. We present the Iterative Domination Solver as an approximate method for generating solution sets that can be used by a human decision maker to meet the goals of a mission. We show that our approximate algorithm performs well across a range of uncertainty parameters, with orders of magnitude less execution time than existing solutions on randomly generated instances.
['Jeffrey Hudack', 'Nathaniel Gemelli', 'Daniel S. Brown', 'Steven Loscalzo', 'Jae C. Oh']
Multiobjective Optimization for the Stochastic Physical Search Problem