Enhanced dung beetle optimization algorithm and its application in 3D UAV path planning

Dung beetle optimization (DBO) algorithm is a population-based metaheuristic that excels in optimization tasks, demonstrating strong performance in finding high-quality solutions. However, when addressing complex optimization problems, the algorithm also suffers from issues such as low population diversity, a tendency to fall into local optima, and suboptimal convergence speed. To address these challenges, this paper proposed an enhanced dung beetle optimization (EDBO) algorithm. The core improvements included the use of the Sobol sequence for better population initialization and the introduction of a mutation mechanism based on probability ratios and random angles to enhance the global exploration ability and convergence accuracy of the algorithm. Furthermore, the convergence factor has been modified to ensure that the algorithm emphasizes global search in the early stages, while focusing on local exploitation in the later stages. Additionally, an adaptive T-distribution mutation method has been constructed to increase the algorithm's probability of escaping from local optima. To validate the performance of the enhanced dung beetle optimization, the algorithm has been tested on the CEC2014 and CEC2017 benchmark functions. The experimental results demonstrated that EDBO outclassed both the original DBO and other advanced optimization algorithms in terms of overall performance. Finally, the effectiveness of EDBO in solving real-world problems is verified through its application in the three-dimensional (3D) path planning of unmanned aerial vehicles. In the most challenging task scenarios, the EDBO algorithm successfully identified feasible paths. Compared with five benchmark algorithms, it achieved an average reduction in the overall cost of 17.1%, 9.5%, 22.9%, 14.5%, and 18.6%, respectively. These results further demonstrated the significant superiority and practical value of the EDBO algorithm in addressing the unmanned aerial vehicles (UAVs) path planning problem.