In this study, different methods entitled Centroidal Voronoi Tessellations and Levenberg-Marquardt applied on SP-QPSO separately to enhance its performance and discovering the optimum point and maximum/ minimum value among the feasible space. Although the results of standard SP-QPSO shows its ability to achieve the best results in each tested problem in local search as well as global search, these two mentioned techniques are applied to compare the performance of managing initialization part versus convergence of agents through the searching procedure respectively. Moreover, because SP-QPSO is tested on low dimensional problems in addition to high dimensional problems SP-QPSO combined with CVT as well as LM, separately, are also tested with the same problems. To confirm the performance of these three algorithms, twelve benchmark functions are engaged to carry out the experiments in 2, 10, 50, 100 and 200 dimensions. Results are explained and compared to indicate the importance of our study.