Application of BBPSO-ABC Optimization Algorithm in Estimation of Dredging Volume of Trailing Suction Hopper Dredger

Since the settlement process of the dredger is a slow and dynamic process, the loading operation status of the dredger cannot be obtained in real time by the sensor. Understanding the internal loading process of the trailing suction hopper dredger (TSHD) is crucial to improve the efficiency of dredging and carry out green dredging. Based on the dynamic deposition model of the TSHD, this paper proposes a new algorithm (BBPSO-ABC), which combines the backbone particle swarm (BBPSO) and artificial bee swarm (ABC), and uses particle search method to find new particles in the follow-up and reconnaissance stages of the artificial bee swarm algorithm introduced into the backbone particle swarm. The results demonstrate that the BBPSO-ABC optimized filter algorithm is more accurate and close to the true value in the state estimate of the loading process when compared to the three current standard improved particle swarm optimization algorithms.