Nondominated-Solution-based Multi-objective Greedy Sensor Selection for Optimal Design of Experiments

Abstract

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

Accepted manuscript for publication in IEEE Transactions on Signal Processing

References in corpus (6)

• Data-Driven Optimal Sensor Placement for High-Dimensional System Using Annealing Machine
• Data-Driven Sensor Selection Method Based on Proximal Optimization for High-Dimensional Data With Correlated Measurement Noise
• Optimization of Sparse Sensor Placement for Estimation of Wind Direction and Surface Pressure Distribution Using Time-Averaged Pressure-Sensitive Paint Data on Automobile Model
• Seismic Wavefield Reconstruction based on Compressed Sensing using Data-Driven Reduced-Order Model
• Proof-of-concept Study of Sparse Processing Particle Image Velocimetry for Real Time Flow Observation
• Effcient magnetometer sensor array selection for signal reconstruction and brain source localization

Cited by in corpus (6)

• Data-Driven Sensor Selection Method Based on Proximal Optimization for High-Dimensional Data With Correlated Measurement Noise
• Optimization of Sparse Sensor Placement for Estimation of Wind Direction and Surface Pressure Distribution Using Time-Averaged Pressure-Sensitive Paint Data on Automobile Model
• Proof-of-concept Study of Sparse Processing Particle Image Velocimetry for Real Time Flow Observation
• Randomized Group-Greedy Method for Large-Scale Sensor Selection Problems
• Observation Site Selection for Physical Model Parameter Estimation toward Process-Driven Seismic Wavefield Reconstruction
• Fast Data-driven Greedy Sensor Selection for Ridge Regression