Selfish herd optimizer (SHO) is a new metaheuristic optimization algorithm for solving global optimization problems. In this paper, an elite opposition-based Selfish herd optimizer (EOSHO) has been applied to functions. Elite opposition-based learning is a commonly used strategy to improve the performance of metaheuristic algorithms. Elite opposition-based learning enhances the search space of the algorithm and the exploration of the algorithm. An elite opposition-based Selfish herd optimizer is validated by 7 benchmark functions. The results show that the proposed algorithm is able to obtain the more precise solution, and it also has a high degree of stability.