An Ensemble of Swarm‐Based Evolutionary Learning Strategies for UAV Path‐Planning Problem

The increasing application of unmanned aerial vehicles (UAVs) in diverse domains demands highly robust and autonomous path‐planning algorithms capable of navigating complex and dynamic environments. To address the multifaceted challenges posed by obstacle avoidance, energy constraints, and environmental uncertainty, this work proposes an ensemble of learning strategies for the optimal path planning of UAVs. We introduce a modular particle swarm optimization and differential evolution (PSO‐DE) ensemble framework and systematically investigate the impact of multiple learning and adaptation strategies, such as chaotic parameter adaptation, opposition‐based learning (OBL), and a range of DE mutation schemes, to enhance the optimization process. We perform extensive experimentation across 16 carefully designed scenarios with varying complexity against ten competitive algorithms. We demonstrate that the integration of the PSO‐DE hybrid with the opposition‐based learning (OBLPSODE) achieves faster convergence while maintaining superior solution quality across all scenarios. The proposed OBLPSODE algorithm substantially outperforms other hybrid variants in both computational efficiency and path optimality, particularly excelling in cluttered environments where traditional algorithms often converge prematurely. Beyond algorithmic contributions, this work provides critical complexity analysis identifying obstacle‐checking operations as the primary computational bottleneck in UAV path planning. The findings offer practical guidance for deploying UAVs in real‐world applications and establish transferable design principles for developing adaptive meta‐heuristics in complex optimization domains.