Research on Multi target Attack Path Planning and Energy Consumption for UAV Based on Improved Sparrow Search Algorithm

Unmanned Aerial Vehicle (UAV) have gained widespread application across various fields due to their low cost, high maneuverability, and ease of operation. However, challenges remain in optimizing path planning and energy consumption for UAV multi target attacks. The Sparrow Search Algorithm (SSA) is a novel metaheuristic optimization approach that emulates the social interaction and flight patterns of sparrows to maximize search efficiency. This paper proposed an Improved Sparrow Search Algorithm (ISSA) that first utilizes the Singer chaos ma p to enhance the initial population distribution, thereby increasing diversity and preventing premature convergence. Secondly, a Levy flight strategy is employed to strengthen the algorithm's ability to escape from local optima. Lastly, a Grey Prediction Model dynamically adjusts predictions based on current states and environmental changes. Simulations and experimental results demonstrate the superiority of our proposed algorithm in multi target path planning and energy consumption on 3D terrains compared to established algorithms.