The Grey Wolf Optimizer Algorithm Modification for Enhanced Performance of Autonomous Underwater Vehicles in a Physical Field Survey
- 10.2991/itids-19.2019.33How to use a DOI?
- Grey Wolf Optimizer, swarm algorithms, autonomous unmanned underwater vehicles control, autonomous unmanned underwater vehicles, optimization algorithms component.
The purpose of the study is to modify the Grey Wolf Algorithm. It is aimed at increasing the speed of the mission execution in order to examine a physical field and detect anomalies. It should consider the limitations imposed by the physical model of the group of autonomous unmanned underwater vehicles operation. For this aim, the simulator is deigned in an integrated development environment NetBeans for Java programming language. To assess the algorithm efficiency, a computational experiment was performed using several types of real physical fields as the source data. The simulation outcomes were compared to those obtained by the tack survey method. The study shows that the proposed algorithm provides better management of a group of autonomous uninhabited underwater vehicles than the well-established tack method of examining the physical field. The modified Grey Wolf Algorithm used in applied problems within the physical field survey proved to be beneficial and reliable. In spite of satisfactory test results, it is necessary to confirm the usage of the algorithm by real autonomous unmanned underwater vehicles. It is required to continue investigating the operation of the Grey Wolf Algorithm in a dynamically changing environment and the influence of different control functions on it.
- © 2019, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Anton Tolstikhin AU - Sergey Bakhvalov AU - Andrey Dorofeev AU - Ruslan Bazhenov PY - 2019/05 DA - 2019/05 TI - The Grey Wolf Optimizer Algorithm Modification for Enhanced Performance of Autonomous Underwater Vehicles in a Physical Field Survey BT - Proceedings of the 7th Scientific Conference on Information Technologies for Intelligent Decision Making Support (ITIDS 2019) PB - Atlantis Press SP - 184 EP - 190 SN - 1951-6851 UR - https://doi.org/10.2991/itids-19.2019.33 DO - 10.2991/itids-19.2019.33 ID - Tolstikhin2019/05 ER -