Multi-UAV Cooperative Landing Path Planning Based on a Multi-Strategy Improved Grey Wolf Optimizer

Cooperative landing of multiple unmanned aerial vehicles (UAVs) requires paths that avoid threats, keep safe separations, remain flyable, and allow the swarm to reach its initial approach points at a common time. This paper proposes a multi-strategy improved grey wolf optimizer (GWO), termed MSGWO, for this three-dimensional path planning problem. A multi-population mechanism groups wolves with similar path characteristics and lets each group follow its own leaders, the nonlinear control parameter of the modified GWO (mGWO) is adopted, and the individual memory of particle swarm optimization (PSO) retains the best position found by each wolf. An explicit constrained joint model uses Deb’s feasibility rules and dense verification. Across six static-threat scenarios with 30 runs each, MSGWO achieves 73.3% feasibility versus 0–55.6% for six comparators, significantly higher after Holm correction, and the best overall Friedman rank. Mutually feasible objective values are significantly lower than GWO, mGWO, and success-history-based adaptive differential evolution with linear population size reduction (L-SHADE) overall, although L-SHADE is better in one scenario. Independent planning and an adapted disturbance-based modified GWO (DMGWO) find no jointly feasible solution in this setting. An ablation study shows that the multi-population mechanism contributes most.