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In recent years, the prevalence of Many-Objective Optimization Problems (MaOPs) in practical applications has been increasing. However, traditional multi-objective optimization algorithms, such as Multiple Objective Particle Swarm Optimization (MOPSO), often face challenges of dimensionality and selection pressure when handling MaOPs. To overcome these challenges, this study proposes a Convergence-Driven Adaptive Many-Objective Particle Swarm Optimization (CDA-MOPSO) algorithm. This algorithm introduces a convergence metric to assess the convergence status and solution distribution quality of the particle swarm during iterations. Based on this metric, Convergence-Aware Learning Factor Adjustment (CALFA), Convergence-Oriented Dimension Variation Strategy (CODVS), and Convergence-Driven Archive Maintenance (CDAM) operations are proposed. Additionally, evolutionary search is further conducted on the external archive to enhance algorithm performance. To validate the performance of the CDA-MOPSO algorithm, extensive experiments are conducted using standard test problems such as DTLZ and WFG. Experimental results demonstrate that the CDA-MOPSO algorithm exhibits superior convergence and solution distribution characteristics across multiple standard test functions, particularly in handling many-objective optimization problems, outperforming traditional multi-objective algorithms significantly. In conclusion, the CDA-MOPSO algorithm provides a novel solution for many-objective optimization problems, offering strong convergence capability and solution diversity, with broad prospects for practical applications.

The use of satellite imagery for surface deformation monitoring has been steadily increasing. However, the study of extracting deformation slopes from deformation data requires further advancement. This limitation not only poses challenges for subsequent studies but also restricts the potential for deeper exploration and utilization of deformation data. The LT-1 satellite, China’s largest L-band synthetic aperture radar satellite, offers a new perspective for monitoring. In this study, we extracted deformation slopes in Chongqing and its surrounding areas of China based on deformation data generated by LT-1. Twelve factors were selected to analyze their influence on slope deformation, including elevation, topographic position, slope, landcover, soil, lithology, relief, average rainfall intensity, and distances to rivers, roads, railways, and active faults. A total of 5863 deformation slopes were identified, covering an area of 140 km2, mainly concentrated in the central part of the study area, with the highest area density reaching 0.22%. Among these factors, average rainfall intensity was found to have the greatest impact on deformation slope. These findings provide valuable information for geological disaster early warning and management in Chongqing and surrounding areas, while also demonstrating the practical value of the LT-1 satellite in deformation monitoring.