Enhanced Spider Wasp Optimizer Based on Tangent Guidance for UAV Path Planning

UAV path planning technology is a key technical assurance that enables unmanned aerial vehicles to navigate intelligently and complete tasks safely and smoothly. This paper presents an enhanced Spider Wasp Optimizer based on tangent guidance, specifically applied to UAV path planning. First, to address the challenge of relying on heuristic experience to determine the number of trajectory points in static global path planning, we introduce a tangent-based method for calculating the number of trajectory points. This approach enables the algorithm to dynamically calculate the number of trajectory points according to the terrain's complexity, thereby enhancing the algorithm's overall efficiency. Second, to enhance the convergence speed, accuracy, and solution quality, we design different probabilistic selection factors tailored to the characteristics of each individual. These factors guide individuals in selecting the most appropriate update model. In addition, we introduced the concept of neighborhood in the model update to prevent the deterioration of candidate solution quality caused by the random selection of individuals for position updates, thereby slowing down the convergence speed of the algorithm. Finally, to address the issue of reduced population diversity in the later stages of the original algorithm, we propose a diversion mechanism that reallocates under performing individuals to enhance population diversity. Simultaneously, we apply the Firefly Algorithm to further refine higher-performing individuals, thereby ensuring the algorithm's convergence speed. To evaluate the performance of the proposed algorithm, we designed experimental scenarios with varying levels of difficulty, demonstrating its effectiveness.