Multiobjective Optimization for the Stochastic Physical Search Problem [chapter]

Jeffrey Hudack, Nathaniel Gemelli, Daniel Brown, Steven Loscalzo, Jae C. Oh
2015 Lecture Notes in Computer Science  
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
more » ... ss execution time than existing solutions on randomly generated instances.
doi:10.1007/978-3-319-19066-2_21 fatcat:sbbnvkjnfzfuxpkrw2vypmauci