Nondominated-Solution-Based Multi-Objective Greedy Sensor Selection for Optimal Design of Experiments

In this study, a nondominated-solution-based multi-objective greedy method is proposed and applied to sensor selection problem based on the multiple indices of optimal design experiments. The simultaneously considers set functions applies idea Pareto ranking for sets. Specifically, new index iteratively added nondominated solutions sets, are evaluated selected from examined solutions, next sets then considered. With procedure, optimization can be conducted with reasonable computational costs. This paper defines class algorithms which includes algorithm group algorithm, characteristics those theoretically discussed. Then, its performance evaluated. results test case show that not only gives Pareto-optimal front but also produces sensors in terms D-, A-, E-optimality, superior by pure methods consider single objective function.