UAV Path Planning: A Dual-Population Cooperative Honey Badger Algorithm for Staged Fusion of Multiple Differential Evolutionary Strategies

To address the challenges of low optimization efficiency and premature convergence in existing algorithms for unmanned aerial vehicle (UAV) 3D path planning under complex operational constraints, this study proposes an enhanced honey badger algorithm (LRMHBA). First, a three-dimensional terrain model incorporating threat sources and UAV constraints is constructed to reflect the actual operational environment. Second, LRMHBA improves global search efficiency by optimizing the initial population distribution through the integration of Latin hypercube sampling and an elite population strategy. Subsequently, a stochastic perturbation mechanism is introduced to facilitate the escape from local optima. Furthermore, to adapt to the evolving exploration requirements during the optimization process, LRMHBA employs a differential mutation strategy tailored to populations with different fitness values, utilizing elite individuals from the initialization stage to guide the mutation process. This design forms a two-population cooperative mechanism that enhances the balance between exploration and exploitation, thereby improving convergence accuracy. Experimental evaluations on the CEC2017 benchmark suite demonstrate the superiority of LRMHBA over 11 comparison algorithms. In the UAV 3D path planning task, LRMHBA consistently generated the shortest average path across three obstacle simulation scenarios of varying complexity, achieving the highest rank in the Friedman test.