Determining glacier ice thickness and the extent of freezing in subglacial sediments is crucial in glaciological studies. Non-invasive geophysical methods, such as Multichannel Analysis of Surface Waves (MASW), are typically used for these tasks. In this study, we introduce a novel metaheuristic called Hunger Games Search (HGS), which simulates hunger-driven instincts and behavioral decisions of animals for the full-parametric inversion of seismic surface waves. We applied HGS to determine layer thicknesses, densities, shear wave velocities, and primary wave velocities of different layers through the joint inversion of multi-mode Rayleigh Wave Dispersion Curves (RWDCs). This marks the first study employing HGS for the inversion of dispersion data. Sensitivity studies of model parameters prior to the inversion indicated the necessity of post-inversion uncertainty evaluations to mitigate the effects of varying sensitivity levels. Additionally, a parameter tuning study was carried out to maximize the performance of HGS. Compared with some swarm intelligence-based optimizers (Particle Swarm Optimization, Cuckoo Search, Grey Wolf Optimization, Sparrow Search Optimization, and Whale Optimization Algorithm), HGS outperforms in the inversion of synthetic multi-mode RWDCs with 10% uncertainties with respect to a simulated glacier structure. In real data applications, a dataset acquired at Midtdalsbreen, an outlet of the Norwegian Hardangerj�n ice cap, is inverted using the tailored HGS metaheuristic. The results obtained are consistent with previous geophysical studies. Furthermore, our analysis reveals that the performance of HGS dealing with the real application is not highly sensitive to the selection of layers within the range of five to eight. The accuracy of HGS is undoubtedly contaminated by a larger model space; however, additional depth information derived from co-located GPR data can be directly integrated into HGS to obtain satisfactory results again.