For the improvement of ordinary PSO, using PSO at the basis of a two-step method is applied by many people. First, the PSO method is used to do a global search, and then a second algorithm is applied which is good at processing locally. The solution obtained by PSO is regarded as the initial solution for further optimization. The problem is that after the global convergence, the solution is difficult to jump out of the region convergence. In conclusion, the conventional two-step method is better than routine PSO, but it still cannot improve the performance of PSO, essentially because it has been assumed that the PSO convergence region belongs to the optimal region. For example, there are three zones (, , and ) in the solution set—, , and belong to , , and , respectively. is better than , and is better than ; if the PSO method could only find , the second algorithm would definitely find , yet it has not jumped out of to find the best solution in the optimal zone. By this way, all particles only rely on the good information, which means the particle moves to the best position in the local or global area. That would be premature aggregation and lost diversity and it would not be the best optimization.