Neighborhood elite learning multi-strategy cooperative particle swarm optimization for coordinated simultaneous arrival path planning of multiple amphibious UAVs

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This paper addresses the coordinated simultaneous-arrival path planning problem for multiple amphibious unmanned aerial vehicles (UAVs) operating under heterogeneous speed constraints in complex amphibious environments. Unlike conventional UAVs, amphibious UAVs must traverse both aerial and aquatic domains, which imposes distinct speed constraints and dynamic adaptability requirements. The objective is to generate collision-free, smooth trajectories that enable all UAVs to reach a common target simultaneously while respecting individual speed limits, avoiding terrain obstacles, and preventing inter-vehicle collisions. To solve this problem, we propose a novel algorithm, termed NEL_MSCPSO (neighborhood elite learning-based multi-strategy cooperative particle swarm optimization). The algorithm integrates hierarchical neighborhood reconstruction with cross-subswarm elite learning, weighted centroid-guided follower updates, fully adaptive parameter adjustment, a hybrid Gaussian-Cauchy mutation scheme with elite protection, adaptive dimensional mutation on the global best, and an enhanced differential evolution-based terminal replacement strategy. Comprehensive experiments on the CEC2022 benchmark suite demonstrate that NEL_MSCPSO achieves the lowest total rank sum among twelve state-of-the-art metaheuristic algorithms. Ablation studies confirm the necessity of each component. More importantly, the algorithm is successfully applied to four multi-amphibious UAV path planning scenarios of increasing spatial complexity, consistently producing feasible trajectories that strictly satisfy simultaneous-arrival constraints under heterogeneous speed profiles. These results demonstrate the superior engineering feasibility and robustness of NEL_MSCPSO for amphibious UAV coordination tasks.

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