Redundancy-Suppressed Multi-Objective Evolutionary Algorithm with Validation-Calibrated Archiving for Sea Surface Temperature Sensor Placement
- 1Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
Abstract
The sea surface temperature sparse sensor placement (SST-SSP) problem involves selecting a limited number of in situ observation sites to reconstruct full sea surface temperature fields. We first establish the SST-SSP model, considering proper orthogonal decomposition (POD) information, deployment burden, and modal redundancy. This is different from well-known POD-based solutions. Then, we propose a cost-aware redundancy-suppressed multi-objective evolutionary algorithm based on decomposition (CARS-MOEA/D) to solve this problem. Specifically, CARS-MOEA/D uses redundancy-assisted decomposition search to explore complementary layouts and validation-calibrated external archiving to retain favorable reconstruction-burden trade-offs. Experiments on 36 Optimum Interpolation Sea Surface Temperature (OISST) instances cover eleven regional and basin-scale domains, as well as the global ocean at three spatial resolutions. The results demonstrate that CARS-MOEA/D outperforms state-of-the-art multi-objective evolutionary algorithms. A Tropical Atmosphere Ocean buoy-network case study further illustrates the practical value of CARS-MOEA/D. The official data sources and source-code repository are provided to support reproducible research and future work in this area.
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BibTeX
@article{shen2026redundancysuppressed,
title = {{Redundancy-Suppressed Multi-Objective Evolutionary Algorithm with Validation-Calibrated Archiving for Sea Surface Temperature Sensor Placement}},
author = {Haotian Shen and Jinsi Zhang and Zuocheng Li},
journal = {Mathematics},
year = {2026},
month = sep,
doi = {10.3390/math14193442},
url = {https://www.mdpi.com/2227-7390/14/19/3442},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Redundancy-Suppressed Multi-Objective Evolutionary Algorithm with Validation-Calibrated Archiving for Sea Surface Temperature Sensor Placement
AU - Haotian Shen
AU - Jinsi Zhang
AU - Zuocheng Li
PY - 2026
DA - 2026/09/22
JO - Mathematics
DO - 10.3390/math14193442
UR - https://www.mdpi.com/2227-7390/14/19/3442
AB - The sea surface temperature sparse sensor placement (SST-SSP) problem involves selecting a limited number of in situ observation sites to reconstruct full sea surface temperature fields. We first establish the SST-SSP model, considering proper orthogonal decomposition (POD) information, deployment burden, and modal redundancy. This is different from well-known POD-based solutions. Then, we propose a cost-aware redundancy-suppressed multi-objective evolutionary algorithm based on decomposition (CARS-MOEA/D) to solve this problem. Specifically, CARS-MOEA/D uses redundancy-assisted decomposition search to explore complementary layouts and validation-calibrated external archiving to retain favorable reconstruction-burden trade-offs. Experiments on 36 Optimum Interpolation Sea Surface Temperature (OISST) instances cover eleven regional and basin-scale domains, as well as the global ocean at three spatial resolutions. The results demonstrate that CARS-MOEA/D outperforms state-of-the-art multi-objective evolutionary algorithms. A Tropical Atmosphere Ocean buoy-network case study further illustrates the practical value of CARS-MOEA/D. The official data sources and source-code repository are provided to support reproducible research and future work in this area.
SN - 2227-7390
ER -