Grey wolf optimizer for unmanned combat aerial vehicle path planning. (September 2016) - Zhang, Sen
Highlights: Unmanned combat aerial vehicle (UCAV) path planning is a fairly complicated global optimum problem. A new meta-heuristic grey wolf optimizer (GWO) is proposed to solve the UCAV path planning problem. The simulation results show that the proposed method is more competent for the UCAV path planning than other state-of-the-art evolutionary algorithms considering the quality, speed, and stability of solutions. Abstract: Unmanned combat aerial vehicle (UCAV) path planning is a fairly complicated global optimum problem, which aims to obtain an optimal or near-optimal flight route with the threats and constraints in the combat field well considered. A new meta-heuristic grey wolf optimizer (GWO) is proposed to solve the UCAV two-dimension path planning problem. Then, the UCAV can find the safe path by connecting the chosen nodes of the two-dimensional coordinates while avoiding the threats areas and costing minimum fuel. Conducted simulations show that the proposed method is more competent for the UCAV path planning scheme than other state-of-the-art evolutionary algorithms considering the quality, speed, and stability of final solutions.