Automatic path planning of unmanned combat aerial vehicle based on double-layer coding method with enhanced grey wolf optimizer

The unmanned combat aerial vehicle (UCAV) technology has to deal with a lot of challenges in complex battlefield environments. The UCAV requires a high number of points to build the path to avoid dangers in order to achieve a safe and low-energy flying path, which increases the issue dimension and uses more computer resources while producing unstable results. To address the issue, this paper proposes a double-layer (DLC) model for path planning, which reduces the outputting dimension of path-forming points, reduces the computational cost and enhances the path stability. Meanwhile, this paper improves the grey wolf optimizer (K-FDGWO) by introducing adaptive K-neighbourhood-based learning strategy and differential “hunger-hunting strategy”, and using fitness distance correlation (FDC) to balance the global exploration and local exploitation. Besides, the K-FDGWO and Differential Evolution (DE) algorithm are jointly used for the DLC model (DLC-K-FDGWO). The experimental results indicated that the proposed DLC-K-FDGWO method for path planning always generated the ideal flight path in complicated environments.