Hybrid optimization algorithm for estimating soil parameters of spoil hopper deposition model for trailing suction hopper dredgers.

This article focuses on optimizing the loading process of trailing suction hopper dredgers (TSHDs) by accurately estimating soil parameters in the spoil hopper sedimentation model using a hybrid optimization algorithm called Simulated Annealing and Multiple Population Genetic Algorithm (SAMPGA). The study demonstrates that SAMPGA outperforms traditional genetic algorithms and other intelligent methods in terms of accuracy, convergence speed, and robustness when estimating key soil parameters affecting dredging efficiency. Validation with real operational data from the Yangtze Estuary shows high agreement between model predictions and measured loading masses, and the estimated soil parameters exhibit good adaptability across different vessel trips within the same dredging area but require re-estimation for different dredging locations. The research provides a methodological foundation for improving TSHD loading performance and offers insights for future dredging operation optimization under varying soil conditions.