A hybrid algorithm based on grey wolf optimizer and differential evolution for UAV path planning

Autonomous navigation is significant to UAVs, especially in disaster scenarios. A Hybrid GWO and Differential Evolution (HGWODE) algorithm is developed to solve UAV path planning. In HGWODE, GWO and DE algorithms cooperate well to balance exploitation and exploration. The position-updated equation of GWO is improved, which makes alpha, beta, and delta wolves search around the alpha wolf, and omega wolves search around the top three wolves to boost the exploitation. A rank-based mutation strategy is implemented in DE algorithm to promote exploitation while maintaining the exploration capacity. We test HGWODE on CEC 2014 and UAV path planning. The proposed HGWODE is superior to GWO and several GWO variants when solving test functions and UAV path planning models. UAV path from HGWODE is smoother and shorter than its rivals.