# A Novel Hybrid High-Dimensional PSO Clustering Algorithm Based on the Cloud Model and Entropy
## 2 Multi-Dimensional Cloud Model
In the meantime, the hybrid PSO algorithm has a global optimization ability and distributed random search characteristics to solve the problems that traditional clustering algorithms easily fall into, including local optimization and sensitive to initial value [11; 12]. Simultaneously, the convergence speed and global optimization ability of the algorithm can be effectively improved through the combination of PSO, which combines excellent features and traditional clustering methods [13; 14; 15; 16; 17; 18; 19]. In order to effectively reduce the algorithm from falling into the local optimum, the multi-mode cooperative multi-objective particle swarm optimization algorithm based on reinforcement learning is proposed. Furthermore, the experimental results show that the hybrid PSO algorithm is more effective and robust than the other algorithms [20]. As a powerful optimization technology, a feature selection clustering method based on particle swarm optimization (PSO) algorithm is proposed [21]. The particle swarm optimization (PSO), based on multi subgroup distributed architecture, is very effective for static multi-objective optimization problems, but it has not been used to solve dynamic multi-objective problems (DMOP) at present [22]. According to the multi-objective particle swarm optimization (MOPSO) algorithm, the design of the updating mechanism and population maintenance mechanism is the critical technology to obtain the optimal solution [23]. In order to effectively solve multi-modal, multi-objective optimization problems with the same fitness value, an input-output fuzzy clustering was proposed [24]. Based on the feature selection algorithm, a new hybrid clustering algorithm for multi-objective PSO feature selection is proposed [25]. Particle swarm optimization has a built-in guidance strategy to improve their solutions [26]. Combined with the automatic clustering problem, a hybrid method of chaos game optimization and particle swarm optimization (CGOPSO) is proposed [27]. Simultaneously, the proposed clustering algorithm is compared with spherical K-means and the PSO algorithm in terms of feasibility and convergence characteristics [28]. However, it is easy to converge to the suboptimal  
\[\Delta C\approx\mu_{A}(x)*\Delta x/(\sqrt{2\pi}En) \tag{2}\]2 The simulation diagram of the multi-dimensional cloud model. clustering solution too early and the learning coefficient value needs to be adjusted to find a better solution [29]. Therefore, the particle swarm optimization (PSO) algorithm is widely used in clustering analysis. By optimizing the algorithm to find the best cluster center, the hybrid ensemble clustering algorithm is used as an optimization problem to solve [30]. At the same time, integrated clustering has been used as the final clustering to achieve high quality. On the basis of data analysis, it is a good idea to improve the performance and the speed in the field of machine learning [31]. At the same time, we all know that the fundamental reason for the decline of clustering performance of imbalanced sets lies in the high-dimensional characteristics of imbalanced data. However, optimization problems show a trend of diversification accompanied by non-linear, high dimensional, and other characteristics. The optimization objectives involved in these practical problems may be one or even several conflicting ones, often with harsh constraints. The PSO algorithm is an intelligent optimization with few adjustable parameters and good robustness. With its outstanding performance in dealing with single objective optimization problems, it has been extended to the field of multi-objective optimization. Through the comparison with the comparison algorithm, it shows that the superiority of the PSO depends on the parameter tuning and it has premature convergence. The clustering evaluation function of data set is regarded as the fitness function of PSO algorithm, which it is a well-known, unsupervised clustering algorithm.