A Self-Learning Slime Mould Algorithm for Robust Multi-UAV 3D Path Planning in Complex Environments

Abstract

The autonomous navigation of UAV swarms in complex environments remains a critical bottleneck for their real-world, cost-effective deployment. This paper introduces a novel metaheuristic swarm intelligence framework, instantiated in the Self-Learning Slime Mould Algorithm (SLSMA), to address this challenge. The SLSMA empowers a UAV fleet to autonomously sense, adapt, and recover from planning failures through three core innovations: a situation-aware search strategy, a collective memory mechanism, and an adaptive recovery behavior. Rigorous evaluation on the CEC 2017 benchmark suite demonstrates that SLSMA achieves the topmost rank in the Friedman test and delivers statistically significant improvements (Wilcoxon rank-sum test, p < 0.05) over eight state-of-the-art metaheuristics. In complex 3D path planning scenarios, SLSMA generates complete collision-free trajectories where competing algorithms fail, achieving a 99.5% mission success rate and reducing the total cost by up to 18.7% compared to the best-performing variant. This work holds substantial importance for the scientific community by providing a foundational shift from static optimization to adaptive, metaheuristic reasoning in swarm robotics. It establishes a new paradigm for resilient autonomy, directly addressing the critical challenge of algorithmic fragility in uncertain environments. The proposed framework offers a replicable architecture that could influence the design of future intelligent systems beyond UAV navigation. The results establish SLSMA not merely as an optimizer, but as a robust solver for resilient autonomy, paving the way for the next generation of autonomous swarms capable of persistent operation in cluttered and dynamic environments.