Three-dimensional path planning for AUV detection of dynamic underwater targets based on multi-strategy whale optimization algorithm

Abstract

To detect underwater targets with low energy consumption in complex ocean environments, a model of seabed topography and currents is established based on the actual ocean environment. Additionally, a model for AUV motion, energy consumption, and sonar detection is established to effectively accomplish the task of detecting underwater targets. To address the issue of insufficient search space with increasing iterations in the Whale Optimization Algorithm, this paper proposes a Multi-Strategy Whale Optimization Algorithm (MSWOA) by incorporating initial reverse learning, stochastic inertia factor, and Cauchy mutation strategy. This modification effectively accelerates convergence speed and prevents convergence to local optimal solutions. Finally, simulation experiments and statistical analysis confirm the effectiveness of the detection target task model proposed in this paper, based on the principle of low energy consumption. The MSWOA exhibits superior performance in terms of solution accuracy, convergence speed, and stability compared to other intelligent optimization algorithms, meeting the path planning requirements for AUV navigation.