HSEPSO: A Hierarchical Self-Evolutionary PSO Approach for UAV Path Planning
Jie Wei, Yuhui Zhang, Wenhong Wei · Proceedings of the Genetic and Evolutionary Computation Conference · 2025
This paper proposes a Hierarchical Self-Evolutionary PSO (HSEPSO) Approach for UAV Path Planning to address the challenges faced by traditional Particle Swarm Optimization (PSO), such as high sensitivity to parameters, the tendency to become trapped in local optima, and slow convergence in later stages. Additionally, existing improvements to PSO lack the ability to dynamically adjust evolution strategies based on the current state of particles. HSEPSO employs a hybrid clustering strategy combining K-Means and DB-SCAN for population initialization, followed by population division based on clustering results. This ensures diversity within the population while enabling particles to focus their search on regions more likely to contain the optimal solution. The algorithm also dynamically adjusts the learning factors and inertia weights through a nonlinear adaptive update strategy, effectively balancing global search and local exploitation. Moreover, based on the real-time state of the particles, HSEPSO incorporates different evolutionary strategies to accelerate convergence, optimize the search for solutions, and enhance algorithm robustness. Experimental results demonstrate that, compared to traditional PSO and other improved algorithms (such as MFIPSO, SDPSO, and SA2PSO), HSEPSO shows notable improvements in optimization performance, convergence speed, and robustness.