A Novel Dynamic Clustering Method by Integrating Marine Predators Algorithm and Particle Swarm Optimization Algorithm

• Published in: IEEE access Vol. 9; pp. 3557 - 3569
• Main Authors: Wang, N., Wang, J. S., Zhu, L. F., Wang, H. Y., Wang, G.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Clustering; Clustering algorithms; Clustering methods; Coding; Datasets; Dynamic clustering; Encoding; Evolutionary algorithms; Evolutionary computation; Heuristic algorithms; Indexes; marine predator algorithm; Optimization; Particle swarm optimization; particle swarm optimization algorithm; Penalty function; Performance enhancement; Predators; Search algorithms; Strategy
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3047819

Data clustering is the process of identifying natural groupings or clusters based on a certain similarity measure in muti-dimensional data. Aiming at the dynamic clustering problem where the number of clusters cannot be determined in advance, a hybrid dynamic clustering method based on the marine predators algorithm (MPA) and particle swarm optimization (PSO) algorithm was proposed. The position update strategy of the PSO algorithm was used to make up for the lack of MPA in global searching. The fixed-length coding strategy with the real number coding method was used to deal with the variable length clustering optimization problem, and the unfeasible solution processing strategy and the penalty function strategy are adopted to improve the performance of the algorithm and achieve simultaneous optimization of the number of clusters and cluster centers. The proposed MPA-PSO algorithm with PSO algorithm, MPA, Differential Evolution (DE) algorithm, Spotted Hyena Optimizer (SHO), Lightning Searching Algorithm (LSA) and Equilibrium Optimizer (EO) are adopted to carry out the clustering simulation experiments on four artificial data sets and six real data sets (Iris, Wine, Wisconsin breast cancer, Vowel, Seeds, and Wdbc) in UCI databases. Three performance indicators (the number of clusters, ARI and Accuracy) are used to evaluate the clustering results. The experimental results show that the proposed method can not only successfully find the correct number of clusters, but also obtain stable results for most test problems.