Developing an “efficient” and/or “effective” search algorithm for enhancing the performance of an intelligent system has been a critical issue today. Metaheuristic algorithms, which can be used as a supervised or unsupervised learning algorithm, provide a feasible way for finding an approximate solution of optimization problems. This talk will be aimed at enhancing the performance of metaheuristic algorithms, by introducing two frameworks of metaheuristic algorithms: pattern reduction and search economics. The former is for reducing the computation time while the latter is for improving the quality of the end results.