# A Novel Hybrid High-Dimensional PSO Clustering Algorithm Based on the Cloud Model and Entropy
## 2 Multi-Dimensional Cloud Model
The simulation results of its multi-objective function can be shown in Figure 3 as follows. In the multi-objective PSO algorithm, it is often difficult to get rid of the local optimum, the accuracy of the solution is not enough, the calculation speed is not fast enough, and so on. As the number of problem objectives increases, the dimension and scale of the solution increase accordingly, resulting in high complexity of the problem and greater difficulty in solving it. How to effectively solve the quality and efficiency of complex optimization problems based on swarm intelligence algorithms has become a hotspot in this field. For multi-objective optimization problems, we should not only solve the balance between diversity and convergence, but also consider the optimal particle selection strategy and diversity maintenance mechanism and other issues effectively. Through a series of experiments, the convergence of different PSO algorithms with different particle sizes can be represented in Figure 4 as follows. Figure 3: The simulation results of multi-objective function. Through experiments, we found that the multi-objective PSO algorithm has the advantages of a small number of individuals, fast iterative convergence, simple operation, and easy implementation. At the same time, we found that the PSO is a random search algorithm based on probability, which has strong robustness and global optimization ability. In the process of generating initial particles, the strategy makes full use of the correlation between features to make the initial population more competitive. Therefore, the multi-objective PSO algorithm has a wide range of applications. However, when dealing with feature selection of high-dimensional data, most of the existing feature selection methods based on the PSO algorithm are prone to fall into local optimization, high computational cost, premature convergence, and low search efficiency. Some scholars also proposed intelligent clustering technology to improve the convergence performance of clustering technology [16]. It is very difficult for classical clustering algorithms to process high-dimensional data. The hybrid algorithms should consider all features of the data and the correlation of all features. The PSO algorithm guides particles to move in the search space according to the individual optimal position and the global optimal position. Concurrently, this strategy is simple and efficient, which easily leads to the problem of particle oscillation in the search process. Thus, the search efficiency of the particle swarm optimization algorithm is reduced and some solutions with better performance are missed. Furthermore, the core idea of the hybrid clustering algorithm is to generate a large number of high-quality feature subsets using the correlation information of features. It not only has a good clustering effect, but also has good dimension reduction efficiency when facing unbalanced data. Thus, it can be applied to the clustering problem of unbalanced data. Due to the unordered range of clustering data, it is impossible to compare the numerical values. The distance-based measurement method cannot be used to measure the similarity between objects. Simultaneously, we will study the clustering algorithm based on information entropy for the above clustering data. Based on the PSO algorithm, the cloud model is applied to deal with uncertainty with the idea of information entropy. The lowest value of information entropy is selected as the classification class, and the class with higher information entropy is used as the next target object, and so on, until a cluster that matches the k value is found. Furthermore, the idea of this clustering algorithm is based on the theory of information entropy, and the average information gain rate is proposed according to the relevant knowledge of information entropy. The highest value of mean information gain rate is selected as the standard of equivalence classification.