Redundancy-Suppressed Multi-Objective Evolutionary Algorithm with Validation-Calibrated Archiving for Sea Surface Temperature Sensor Placement

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.