MSGSO: A Multi-Strategy Glider Snake Optimizer for Global Optimization and 3D UAV Path Planning

Information in the Glider Snake Optimizer (GSO) propagates through a leader-predecessor chain. This structure is simple, but it can lose diversity when adjacent agents converge to the same region. The proposed Multi-Strategy Glider Snake Optimizer (MSGSO) retains the original GSO update and subsequently applies nonlinear dynamic polynomial mutation (NDM), elite opposition-based learning (EOBL), and quadratic interpolation (QI). MSGSO was evaluated on CEC 2019 and CEC 2022 with 30 matched random seeds and 15,000 objective evaluations for every algorithm-problem pair. The experiments included seven alternative optimizers and all single, pairwise, and three-operator GSO variants. Across the 34 benchmark problems, MSGSO significantly outperformed GSO on 31 and showed no significant loss. It nevertheless ranked third in each external comparison: L-SHADE led CEC 2019 and CEC 2022 at D=10, while CMA-ES led CEC 2022 at D=20. The ablation attributed most of the gain to NDM; NDM-GSO led the GSO variants on CEC 2019, and NDM-QI-GSO led at both CEC 2022 dimensions. In the UAV study, MSGSO returned 29 feasible paths in 30 runs in Scenario 1 and feasible paths in every run in the other two scenarios. It led the feasibility-first ranking in Scenarios 1 and 2 and placed third in Scenario 3. Thus, MSGSO improves its parent algorithm, although the full three-operator sequence is not consistently the best configuration.