Unmanned Aerial Vehicles 3D Path Planning Based on Improved Hippopotamus Optimization

In the context of 3D path planning for UAVs, many meta-heuristic algorithms struggle to effectively address challenges in complex scenarios, which necessitates enhancements to these algorithms. Although the Hippopotamus Optimization (HO) algorithm has garnered positive reviews since its inception, it exhibits notable shortcomings, including limited search capabilities and an inability to effectively escape from local optima. To mitigate these issues, this paper presents a multi-strategy improved Hippopotamus Optimization (IHO). First, the SPM chaotic mapping strategy generates the initial population, which enhances the diversity of the population. Second, the Northern Goshawk search strategy is integrated to enhance the global exploration capabilities of HO. Third, a Logistic chaotic mapping strategy is used to generate predator positions, further improving the diversity of the latter half of the population. Fourth, a tangent flight strategy is adopted to strengthen the algorithm's global search capabilities. Finally, an adaptive lung performance search strategy is introduced to replace the exploitation phase of HO, enhancing its ability to escape from local optima and accelerating the convergence speed. The IHO underwent multiple experiments using the CEC2017 test sets and was compared with five other algorithms, demonstrating its effectiveness. Using Digital Elevation Model (DEM) maps from the three regions, six scenarios of varying complexities were constructed. The results indicated that, compared with HO, the IHO achieved an average reduction of 42.12% in standard deviation and a 6.19% decrease in total cost. The IHO demonstrated significant advantages over other algorithms, confirming its robustness and practicality.