[1]JIA Heming,ZHANG Zongqi,JIANG Zichao,et al.An optimization fuzzy C-means clustering algorithm based on the hybrid identity search and slime mold algorithms[J].CAAI Transactions on Intelligent Systems,2022,17(5):999-1011.[doi:10.11992/tis.202107011]
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An optimization fuzzy C-means clustering algorithm based on the hybrid identity search and slime mold algorithms

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 17 Number of periods: 2022 5 Page number: 999-1011 Column: 学术论文—人工智能基础 Public date: 2022-09-05
Title:
An optimization fuzzy C-means clustering algorithm based on the hybrid identity search and slime mold algorithms
Author(s):
JIA Heming1;  ZHANG Zongqi2;  JIANG Zichao2;  FENG Yuqi2
1. School of Information Engineering, Sanming University, Sanming 365004, China;
2. College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China
Keywords:
fuzzy C-mean clustering; heuristic optimization; slime mold algorithm (SMA); adolescent identity search algorithm (AISA); social mechanism; fuzzy strategy; UCI database; fusion algorithm
CLC:
TP391
DOI:
10.11992/tis.202107011
Abstract:
The fuzzy C-means clustering algorithm (FCM) has many shortcomings, such as high sensitivity to initial clustering centers, slow convergence, unstable clustering results, and ease of falling into local optimums. To address these problems, an adaptive optimization fuzzy C-means algorithm based on the fusion of the slime mold algorithm and the adolescent identity search algorithm (AISA–SMA–FCM) is proposed in this paper. First, the algorithm improves the global search and local development performance of SMA by introducing the youth social mechanism in AISA, thus overcoming the limitation of SMA of not being sensitive to high-dimensional data and some mixed peak data, along with an excellent performance of the improved AISA–SMA algorithm in optimizing and solving problems verified by the standard test function. Second, the novel proposed algorithm adds the AISA–SMA clustering link to the iteration process of FCM, enabling it to have the same characteristics as the adaptive optimization algorithm—undergoing two processes of exploration and development in each iteration, adjust the proportion as per the number of iterations, and solve the clustering results. Finally, through the simulation test on the UCI standard data sets, the stability and effectiveness of the algorithm are evaluated based on the fitness average and the clustering accuracy rate. The results show that the AISA–SMA algorithm demonstrates a good effect when used in the iteration mechanism of the FCM algorithm, with a faster convergence speed and a higher solution accuracy when compared with other clustering methods and corresponding optimization technologies.
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