A meta-heuristic assisted method for the deployment of the multi-BWBUG cooperative system

Blended-wing-body underwater gliders (BWBUGs) are characterized by low noise and high endurance, and they are well suited for deployment in offshore missions. However, the deployment of the multi-BWBUG cooperative system faces great difficulties due to the complex ocean environment and the difficulty of accurately establishing the deployment model. Thus, a novel meta-heuristic assisted method is proposed for the deployment of the multi-BWBUG cooperative system in this study. First, the deployment optimization model of the multi-BWBUG cooperative system is established by combining the detection coverage ratio and the all-terminal reliability. In addition, Levy flight with visual expansion (LVE) is put forward to effectively enhance the search capability of the search unit. Then, an LVE-enhanced whale optimizer (LVEWO) is proposed for improving the quality of the initial population and the search strategy. The embedding of LVE and the well -designed competition mechanism can improve the convergence speed of LVEWO. Simulation experiments and statistical analysis results convincingly demonstrate the effectiveness of the proposed deployment model and the competency of LVEWO to deploy the multi-BWBUG cooperative system. The comprehensive performance of LVEWO is better than those of the FA, EABC, and WO algorithms.

Automated summary

A novel meta-heuristic assisted method is proposed for the deployment of multi-blended-wing-body underwater gliders (BWBUGs) in this study. The deployment optimization model is established by combining the detection coverage ratio and all-terminal reliability, and a Levy flight with visual expansion (LVE) is used to enhance the search capability. An LVE-enhanced whale optimizer (LVEWO) is proposed for improving the quality of the initial population and the search strategy. Simulation experiments demonstrate the effectiveness of the proposed deployment model and the competency of LVEWO. The comprehensive performance of LVEWO is better than other algorithms.