Path Planning for Uninhabited Combat Aerial Vehicle Using Hybrid Meta-Heuristic DE/BBO Algorithm

Path planning for uninhabited combat aerial vehicle (UCAV) is a complicated high dimension optimization problem, which mainly centralizes on optimizing the flight route considering the different kinds of constrains under complicated battle field environments. Original biogeography-based optimization (BBO) is used to solve the UCAV path planning problem. Furthermore, a new hybrid meta-heuristic differential evolution (DE) and BBO algorithm is proposed to solve the UCAV path planning problem. DE is applied to optimize the habitat migration and mutation operation of the improved BBO model during the process of habitat HSI updating. Then, the UCAV can find the safe path by connecting the chosen nodes of the coordinates while avoiding the threat areas and costing minimum fuel. This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic BBO. The realization procedure for original BBO and this hybrid meta-heuristic approach DE/BBO is also presented. To prove the performance of this proposed hybrid meta-heuristic method, DE/BBO is compared with BBO and other population-based optimization methods, such as, ACO, DE, ES, GA, PBIL, PSO and SGA. The experiment shows that the proposed approach is more effective and feasible in UCAV path planning than the other model.