Three-Dimensional Path Planning for UAV Based on Multi-Strategy Dream Optimization Algorithm

作者：Xingyu Yang, Shiwei Zhao, Wei Gao, Peifeng Li, Zhe Feng, Lijing Li, Tongyao Jia, Xuejun Wang · 发表于：Biomimetics · 年份：2025 · DOI：10.3390/biomimetics10080551 · 被引用次数：10 · 研究领域：Robotic Path Planning Algorithms、UAV Applications and Optimization、Metaheuristic Optimization Algorithms Research

The multi-strategy optimized dream optimization algorithm (MSDOA) is proposed to address the challenges of inadequate search capability, slow convergence, and susceptibility to local optima in intelligent optimization algorithms applied to UAV three-dimensional path planning, aiming to enhance the global search efficiency and accuracy of UAV path planning algorithms in 3D environments. First, the algorithm utilizes Bernoulli chaotic mapping for population initialization to widen individual search ranges and enhance population diversity. Subsequently, an adaptive perturbation mechanism is incorporated during the exploration phase along with a lens imaging reverse learning strategy to update the population, thereby improving the exploration ability and accelerating convergence while mitigating premature convergence. Lastly, an Adaptive Individual-level Mixed Strategy (AIMS) is developed to conduct a more flexible search process and enhance the algorithm's global search capability. The performance of the algorithm is evaluated through simulation experiments using the CEC2017 benchmark test functions. The results indicate that the proposed algorithm achieves superior optimization accuracy, faster convergence speed, and enhanced robustness compared to other swarm intelligence algorithms. Specifically, MSDOA ranks first on 28 out of 29 benchmark functions in the CEC2017 test suite, demonstrating its outstanding global search capability and conver-gence performance. Furthermore, UAV...