An improved constrained differential evolution algorithm for unmanned aerial vehicle global route planning

• The global route planning problem for unmanned aerial vehicle (UAV) is formulated as a constrained optimization problem with multiple threats and terrain obstacles taking into account.
• An improved constrained differential evolution (DE) algorithm, which combines DE algorithm with the level comparison method, is proposed to find the optimal route in feasible regions.
• Numerical experimental in two-dimensional and three-dimensional scenarios are carried out. The effectiveness and robustness of the improved algorithm are demonstrated by comparing its performance with the original DELC algorithm and other numerous bio-inspired algorithms.

This paper formulates the global route planning problem for the unmanned aerial vehicles (UAVs) as a constrained optimization problem in the three-dimensional environment and proposes an improved constrained differential evolution (DE) algorithm to generate an optimal feasible route. The flight route is designed to have a short length and a low flight altitude. The multiple constraints based on the realistic scenarios are taken into account, including maximum turning angle, maximum climbing/gliding slope, terrain, forbidden flying areas, map and threat area constraints. The proposed DE-based route planning algorithm combines the standard DE with the level comparison method and an improved strategy is proposed to control the satisfactory level. To show the high performance of the proposed method, we compare the proposed algorithm with six existing constrained optimization algorithms and five penalty function based methods. Numerical experiments in two test cases are carried out. Our proposed algorithm demonstrates a good performance in terms of the solution quality, robustness, and the constraint-handling ability.