An Energy and Fuel-effective Solution for School Exploration of a Fishing Vessel Through Swarm Intelligence Approach

Abstract

In recent years, energy and fuel efficiencies have been considered in scientific studies. These parameters become extremely important in maritime, especially for fishing vessel activities. In this study, an innovative approach is proposed to reduce the fuel consumed by fishing vessels and carbon emissions to the environment during the fish exploration process. Key elements of the proposed approach are autonomous underwater vehicles (AUVs) and the application of swarm intelligence. With this approach, which can be considered a pioneer in maritime, the AUVs released from the fishing vessel find the school through the swarm intelligence behavior of Grey Wolves. In this article, the method is modeled as a simulation, and its applicability in the future is also discussed. In the present studies, the conventional fish search method and the proposed method were modeled, and the results were examined. When the obtained results are examined, it is seen that the proposed method increases the successful voyage rate by 2.94 times compared to the conventional method, while the distance covered in the exploration activity decreases by 8.61 times. The results demonstrated that the proposed innovative approach is an energy-efficient, cost-effective, and environmentally friendly solution that is also applicable and usable in the future.

Keywords

• energy efficient
• carbon emission
• auvs
• grey wolf algorithm
• swarm intelligence