Multiobjective Optimization for the Stochastic Physical Search Problem

摘要: 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. present Iterative Domination Solver approximate method for generating sets that can be used by human decision maker to meet goals mission. show our algorithm performs well across range uncertainty parameters, with orders magnitude less execution time than existing solutions randomly generated instances.