Three-Dimensional UAV Path Planning Based on Multi-Strategy Integrated Artificial Protozoa Optimizer

• Published in: Biomimetics (Basel, Switzerland) Vol. 10; no. 4; p. 201
• Main Authors: Sun, Qingbin, Feng, Zhihui, Hai, Shiji, Shi, Jinshuo
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 25.03.2025; MDPI
• Subjects: Adaptability; Algorithms; Cauchy mutation strategy; Convergence; dynamic optimal leadership mechanism; Efficiency; Environmental monitoring; Exploitation; Exploration; Flying-machines; Genetic algorithms; Heuristic; Mathematical models; multi-strategy integrated artificial protozoa optimizer; Optimization; refractive opposition-based learning (ROBL); tent map; UAV path planning; United States; New Jersey; UAV path planning; dynamic optimal leadership mechanism; multi-strategy integrated artificial protozoa optimizer; refractive opposition-based learning (ROBL); simulated annealing algorithm; Cauchy mutation strategy; tent map
• ISSN: 2313-7673; 2313-7673
• DOI: 10.3390/biomimetics10040201

Three-dimensional UAV path planning is crucial in practical applications. However, existing metaheuristic algorithms often suffer from slow convergence and susceptibility to becoming trapped in local optima. To address these limitations, this paper proposes a multi-strategy integrated artificial protozoa optimization (IAPO) algorithm for UAV 3D path planning. First, the tent map and refractive opposition-based learning (ROBL) are employed to enhance the diversity and quality of the initial population. Second, in the algorithm’s autotrophic foraging stage, we design a dynamic optimal leadership mechanism, which accelerates the convergence speed while ensuring robust exploration capability. Additionally, during the reproduction phase of the algorithm, we update positions using a Cauchy mutation strategy. Thanks to the heavy-tailed nature of the Cauchy distribution, the algorithm is less likely to become trapped in local optima during exploration, thereby increasing the probability of finding the global optimum. Finally, we incorporate the simulated annealing algorithm into the heterotrophic foraging and reproduction stages, effectively preventing the algorithm from getting trapped in local optima and reducing the impact of inferior solutions on the convergence efficiency. The proposed algorithm is validated through comparative experiments using 12 benchmark functions from the 2022 IEEE Congress on Evolutionary Computation (CEC), outperforming nine common algorithms in terms of convergence speed and optimization accuracy. The experimental results also demonstrate IAPO’s superior performance in generating collision-free and energy-efficient UAV paths across diverse 3D environments.