Research on multi-beam line measurement based on theoretical derivation and particle swarm optimization model
DOI:
https://doi.org/10.54097/9xd9k154Keywords:
Sea area survey; multi-beam line measuring device; Particle swarm optimization modelAbstract
Starting from the geometric model formed by the multi-beam line measuring device for seabed mapping, this paper makes geometric analysis of the two-dimensional plane and three-dimensional map respectively and establishes the mathematical expressions of the relevant parameters, and calculates the mathematical function relationship of the relevant parameters according to the motion of the surveying ship on the sea level and the distribution of the survey lines. Two and three dimensional models are established to improve the accuracy of surveying submarine depth and the utilization rate of multi-beam line measuring device. By using geometric relation in two-dimensional plane, linear function relation is established and related parameters are solved. Based on the working principle of analyzing the solution method of the full coverage shortest path, a benchmark is established, the measured line is perpendicular to the sea boundary, the parameter values at this time are calculated, the dynamic programming model is established, the local optimal solution is obtained by particle swarm optimization algorithm, and the optimal solution is obtained by cyclic traversal. The minimum number of survey lines N and the minimum total measurement length L are obtained
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Shi Lei. Comparative analysis of single beam sounding system and shallow water multi-beam sounding system in underwater topographic survey [J]. Harbin Hydrology and water Resources Survey Station, 2018,46 (5): 32-34
Fu Zuomin, LIU Zhe, Wang Qi, Chen Xiaowei, SHANG Huachao. Application analysis of multi-beam sounding system in ocean Hydrographic survey [J]. Engineering Equipment and Materials, 2019,14:136-137
Luo Jun. Multi-beam and single-beam sounding data fusion processing method [J]. Ocean Surveying and Mapping, 2018,38 (6): 21-24Ma Kunlong. Short term distributed load forecasting method based on big data [D]. Changsha: Hunan University, 2019.
Feng Jijun, Dai Ran, Zhu Jinshan. Several methods of sea area estimation [J]. Journal of Dalian Maritime University, 1006-7736 (2007) su-0100-04
Yang Bowen, Qian Weiyi. Adaptive particle swarm optimization with multiple inertia weights [J]. Journal of Bohai University (Natural Science Edition), 2019, 42 (1): 41-90
Huang Xin, Qiu Gangwei, Tang Weiping. Adaptive Inertial weight particle swarm optimization algorithm based on distance cost [J]. Computer Knowledge and Technology, 2002,18 (5): 52-55
Du Lin, Cao Jiangtao, LI Shuchen. Adaptive particle swarm optimization based on inertial weight matrix [J]. Control Engineering, 2018,25 (7): 1303-1311
Jinwei Q ,Shuzan L ,Ming L , et al. A modified particle swarm optimization algorithm for a vehicle scheduling problem with soft time windows[J]. Scientific Reports,2023,13(1).
Fei H ,Zhang B ,Liu Y , et al. A Novel Chaotic Elite Adaptive Genetic Algorithm for Task Allocation of Intelligent Unmanned Wireless Sensor Networks[J]. Applied Sciences,2023,13(17).
Ge Z ,Junjiang H ,Wenshan L , et al. DGA-PSO: An improved detector generation algorithm based on particle swarm optimization in negative selection[J]. Knowledge-Based Systems,2023,278.
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