The Grasshopper Optimization Algorithm (GOA) is a relatively recent population-based stochastic approach for solving the nonlinear global optimization problems. A number of attempts have been made in the past to improve the efficiency of population-based approaches by combining them with the features of other approaches. In this paper, an attempt has been made to introduce an enhanced version of GOA by combining it with another population-based approach that is the Self-Organizing Migrating Algorithm (SOMA). GOA is combined with SOMA, and a hybrid variant, SOMGOA, is proposed. The effectiveness of this approach is analyzed on the basis of results, and comparative analysis is made against the previously published results by other algorithms on 15 standard benchmark functions. It is concluded that SOMGOA outperforms all and can be used further to solve nonlinear optimization problems.