Adaptive multi-UAV path planning method based on improved gray wolf algorithm. (December 2022)

• Record Type: Journal Article
• Authors: Jiaqi, Shi; Li, Tan; Hongtao, Zhang; Xiaofeng, Lian; Tianying, Xu
• Is Part Of: Computers & electrical engineering. Volume 104:Part A(2022)
• Publication Date: 2022-12
• Subjects: Gray wolf algorithm -- Path planning -- Adaptive -- Convergence time -- Smoothness; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108377
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Aiming at the problem of slow convergence in the gray wolf algorithm, a spiral position update method is introduced. And set the probability p of choosing the update method as the golden ratio. In the original gray wolf algorithm, the number of leadership layers is fixed at 3, which makes the algorithm weaker in search ability. In the iterative process, an adaptive leadership hierarchy was added, including two parts: increasing the leadership hierarchy and decreasing the leadership hierarchy. Improved the convergence speed of the algorithm and the efficiency of completing the task of the UAV. Abstract: Due to the slow convergence and insufficient flight path in path planning, we proposes an adaptive multi-UAV path planning method (AP-GWO) that improves the gray wolf algorithm. The spiral update position method is introduced using the whale algorithm as reference, while the probability of selecting the update method is set to the golden ratio of 0.618. Afterwards, in the iterative process, a different number of leadership levels is used to update the position of the individual, and the leadership is adjusted using an adaptive mechanism. The number of strata balances the process of encirclement and attack. The experimental results show that the proposed AP-GWO method can shorten the flight time of the UAV by an average of 22.8%, shorten the convergence time of the algorithm, and make the flight path of the UAV smoother. Graphical abstract: Image, graphical abstract