## 1 Introduction
In some cases, the entire search area cannot explore a small population size with large iteration but can exploit some small area of search space by doing a local search, so a premature convergence problem happens [15]. Particularly to solving these complex problems, many researchers are devoted to dealing with this issue. The main reason for the premature convergence issue is the lack of diversity in the population [16]. It is very important to make the diversity of the population perfect. Above we have discussed all the applications or fields where it can be used. PSO classifier is not only a tool for solving optimization problems but also a tool to comprise individuals as well as artificial agents socio-cognition related to social psychology principles. A PSO system integrates local search techniques with global search techniques to seek a sort of balance between exploration and exploitation [17]. The main concept of the exploration and exploitation are searching for the unknown solution points are discuss given below. Figure 1: Population initialization using the uniform distribution. Exploration means explore the whole search space to search for a new individual that far from the current individual. It has less chance to trap in the given search space. Exploitation is fewer exploration means explore the limited or restricted area. It utilizing the solution points in hand means to search the new individual in the search space that is nearby the current individual [18]. Premature convergence means that a group of the population while moving in the search space to find optimal solution converged too early [19]. PSO algorithm uses real number coding. Since there's no selection, crossover, and mutation, the algorithm's frame is relatively easy and the velocity of deployment is very quick. However, if some particle in the swarm finds the current optimal position, the other particles will quickly try to gather near it. If the position is a maximum of local, then the swarm of particles will not be able to find it again. Therefore, the algorithm is drowned in the maximum of the local and premature convergence occur [20]. From different perspectives, in each iteration, members of the population-based search algorithm understand the conceptions of exploring and exploiting in three phases: self-creation, collaboration, and competency. In the self-creation step, each individual improves their performance. In the collaboration step, each individual interacts with the other by transferring the information. Finally, with incompetency, each strives to suffer [21]. Generally, these phases are stochastic forms and can be utilized in different ways. These are the core concepts beyond nature-inspired population-based heuristic algorithms. These ideas help an algorithm find the best overall solution. As Swarm Intelligence (SI) methodology, due to the easiness of implementation and the ability to promising optimization, PSO is successfully implemented in a comprehensive scientific analysis and engineering solution. However, the PSO has faced some problems such as the high complexity of the computation, premature convergence, and diversity of the algorithm. In certain cases, a limited population size combined with a large number of iterations is unable to cover the entire search space but can exploit some small area of search space by doing a local search, so a premature convergence problem happens. Particularly to solving these complex problems, many researchers are devoted to dealing with this issue [13]. Like other algorithms of empirical evolution, during the evolution process, the main cause for the premature convergence of Basic PSO is the rapid reduction of diversity of the population. It is very important to make the diversity of the population perfect. In general, there are some techniques for maintaining diversity. When an algorithm is used to measure the diversity of the population, it is a consistent measure to compare the diversity estimated value to different degrees.