Improved Bat Algorithm for UAV Path Planning in Three-Dimensional Space

• Main Authors: Xianjin Zhou, Fei Gao, Xi Fang, Zehong Lan
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Access
• Subjects: Battlefield environment; path planning; improved bat algorithm; convergence; local search
• Online Access: https://ieeexplore.ieee.org/document/9334996/

This paper describes the flight path planning for unmanned aerial vehicles (UAVs) based on the advanced swarm optimization algorithm of the bat algorithm (BA) in a static environment. The main purpose of this work is that the UAVs can obtain an accident-free, shorter, and safer flight path between the starting point and the endpoint in the complex three-dimensional battlefield environment. Based on the characteristics of the standard BA and the artificial bee colony algorithm (ABC), a new modification of the BA algorithm is proposed in this work, namely, the improved bat algorithm integrated into the ABC algorithm (IBA). The IBA mainly uses ABC to modify the BA and solves the problem of poor local search ability of the BA. This article demonstrates the convergence of the IBA and performs simulations in MATLAB environment to verify its effectiveness. The simulations showed that the time required for the IBA to obtain the optimum solution is approximately 50% lower than the BA, and that the quality of the optimum solution is about 14% higher than the ABC. Furthermore, by comparing with other traditional and improved swarm intelligent path planning algorithms, the IBA can plan a faster, shorter, safer, accident-free flight path for UAVs. Finally, this article proves that IBA also has good performance in optimizing functions and has broad application potential.