MO-ALA: A Multi-Objective Artificial Lemming Algorithm for Post-Disaster Mountain UAV Search-and-Rescue Path Planning

Post-disaster mountain UAV search-and-rescue path planning must balance range, energy, threats, search reward, and attitude jitter, forming a multi-constraint, multi-objective problem in 3D terrain with disconnected feasible regions. Existing artificial lemming algorithm (ALA) variants are single-objective, suffering from solution loss, fixed perturbations, and limited diversity. We propose MO-ALA, a multi-objective ALA with dual-layer historical memory and adaptive t-distribution perturbation. Memory improves convergence, while the t-distribution couples a time-varying tail-shape parameter with a decaying amplitude envelope, enabling broad early exploration and precise late search; archive pruning yields a weight-adjustable approximate Pareto set. On ZDT1–ZDT3, MO-ALA outperforms five baselines in IGD; against its ablation variants, however, it is significantly worse than Variant A on ZDT1 and nominally better on DTLZ3 (p = 0.026, not surviving the Holm correction), with no significant difference on the other four benchmarks. The contribution is scoped to non-deceptive fronts: on deceptive DTLZ1/DTLZ3, MO-ALA (like all tested ALA variants) fails to converge (HV ≈ 0). A crossover-based hybrid restores convergence but degrades ZDT precision, remaining an optional remedy. Terrain-scenario ablation attributes the detected benefit to the memory layer, while the t-distribution adds no measurable cost or gain. Path length increases of ~ 21%/6% yield ~ 43%/40% reductions in safety and search-reward cost, respectively, supporting post-disaster deployment.