Abstract: The spider monkey optimization algorithm (SMO) is a swarm intelligence optimization algorithm that can simulate foraging behavior of spider monkey, and has been widely applied in numerical optimization due to its good self-organizing ability. In this paper, a hybrid spider monkey optimization algorithm (QSMO) is proposed, which integrates Metropolis criterion, quadratic approximation method, and local random search strategy with artificial bee colony algorithm to improve the population diversity. A number of standard test functions are selected to verify the effectiveness of the propose QSMO algorithm. By comparing the simulation results, it is shown that the search accuracy and search speed of SMO are significantly improved. Finally, the improved optimization algorithm is utilized to achieve the optimization of the parameters of the industrial acetylene hydrogenation reactor model.