Optimization of multibeam line distribution based on ALNS improved GA

Shuai Ding; Fan Yu; Lingyu Yang; Hong Wang

doi:10.1117/12.3035258

21 July 2024 Optimization of multibeam line distribution based on ALNS improved GA

Shuai Ding, Fan Yu, Lingyu Yang, Hong Wang

Proceedings Volume 13219, Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024); 132191H (2024) https://doi.org/10.1117/12.3035258
Event: 4th International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2024), 2024, Kaifeng, China

Abstract

The conventional method of seabed terrain matching is limited by its underlying principle and practical constraints, resulting in inadequate positioning accuracy and efficiency. To overcome this problem, this study proposes a novel approach to achieve precise distribution of survey lines for multibeam surveys. The approach utilizes an enhanced genetic algorithm of Adaptive Large Neighborhood Search Algorithm (ALNS). This method is of great importance for ensuring the safety of maritime navigation and improving the production of nautical charts. The study begins by establishing a mathematical model for measuring multibeam lines and simulating water depth data. Subsequently, bathymetric data is matched through adaptive selection, and the results are analyzed and calculated using various selection operators. The optimal selection operator is then employed for the final calculation. Finally, the integration of ALNS with the genetic algorithm leads to the optimal scheme for survey line distribution. Experimental findings demonstrate that this optimization method effectively improves measurement accuracy and coverage for multibeam surveys. It offers a viable solution for optimizing seabed topography measurements.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shuai Ding, Fan Yu, Lingyu Yang, and Hong Wang "Optimization of multibeam line distribution based on ALNS improved GA", Proc. SPIE 13219, Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024), 132191H (21 July 2024); https://doi.org/10.1117/12.3035258

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