Multi Drone Simulation with Fish School Search Algorithm

Authors

  • Guntur P. B. Knight Institut Teknologi Del, Indonesia Author
  • Hadumanro Malau University College London, United Kingdom Author
  • Mulya D. G. Ginting Institut Teknologi Del, Indonesia Author

Keywords:

Fish School Search algorithm, Multi-Drone coordination, Swarm intelligence, Python simulation

Abstract

Drone simulation is developed using the Fish School Search (FSS) algorithm. This simulation aims to optimize the coordination of multiple drones for efficient scanning of designated areas, leveraging Swarm Intelligence principles. The motivation behind this study is to determine the optimal number of autonomous systems in surveillance, environmental monitoring, and disaster management, based on the area. The objective is to determine the optimal number of drones required for scanning different area sizes, enhancing the efficiency and effectiveness of the drones used. The area used in this simulation is a rectangular area. The Python programming language is employed to implement and test the FSS algorithm. Results indicate that increasing the number of drones does not linearly decrease the scan time for each area size, highlighting the need for optimized drone coordination. Based on the scenarios used in this simulation it is concluded that the optimal scenario is 6 drones used in 17 x 17 rectangular grid which need 62.721 seconds to cover the entire rectangular area. Surely this simulation can be verified using real experiments using mapping on the C section of the experiment part. 

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Published

2024-12-01

How to Cite

Multi Drone Simulation with Fish School Search Algorithm. (2024). Internetworking Indonesia Journal, 16(2), 11-16. https://internetworkingindonesia.org/index.php/iij/article/view/50