Path Planning Method for Uavs in Enemy-Controlled Threat Areas Based on Multi-Population Evolutionary Slime Mold Algorithm
Mingtian Da, Zhixin Sun · 2025
To address the path planning challenge for UAVs operating in enemy-controlled threat zones, this paper considers various factors, including mountainous terrain, enemy threats, minimum step size, and maximum course deviation. An optimization model for UAV reconnaissance paths in complex mountainous environments is developed, reflecting the constraints encountered during actual combat operations. To solve this model, a Multi-Population Evolutionary Slime Mold Algorithm (MESMA) is proposed. MESMA enhances global search capabilities through a sine-cosine function-based movement mechanism and incorporates multi-population evolutionary strategies to increase diversity and convergence speed, thereby avoiding local optima. The algorithm's accuracy and rapid convergence are validated through tests on six benchmark functions. Applying MESMA to the UAV path planning problem effectively addresses the issues of slow convergence and low precision. Finally, simulation tests are conducted to solve the UAV path planning model in enemy-controlled threat areas.