S-wave velocity (Vs), as a crucial parameter in the study of site effects, has been investigated extensively. In the last decade, the use of the surface wave analysis technique to create Vs profiles has attracted considerable attention due to its success in many case studies. Nevertheless, because of the high nonlinearity of the inversion of surface wave data, the use of the surface wave analysis technique may yield erroneous results. Therefore, it is important to develop an appropriate inversion algorithm to obtain a reasonable Vs profile. To this end, in the current study, a novel inversion algorithm based upon a meta-heuristic algorithm, i.e., artificial jellyfish search (AJS) algorithm, for inverting surface wave dispersion curves is developed. The algorithm is tested on the synthetic data and a real data set. Also, the proposed method has been compared with the particle swarm optimization (PSO)-based inversion algorithm to invert the dispersion curve. The results show that the performance of the AJS-based inversion algorithm in the inversion of synthetic data sets under different conditions (for instance, in the presence of noise, a broad search space, and the presence of a low-speed layer) is fast, stable, and powerful. In addition, the Vs profile estimated from a realistic dispersion curve by the applied algorithm is consistent with the borehole stratigraphic data and the geological information of the study area. Also, the obtained results show that while the performance of the AJS-based inversion algorithm is as good as the PSO-based inversion method, the AJS algorithm requires fewer internal parameter settings and achieves sufficient accuracy in a smaller number of iterations. Thus, it is more time-efficient and easy to implement.