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Derived Multi-population Genetic Algorithm for Adaptive Fuzzy C-Means Clustering

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Abstract

Fuzzy C-Means (FCM) is a common data analysis method, but the clustering effect of this algorithm is easily affected by the initial clustering centers. Currently, scholars often use the multiple population genetic algorithm (MPGA) to optimize the clustering centers, but the MPGA has insufficient global search ability and lacks self-adaptability, is prone to premature convergence, and has poor initial clustering centers. Therefore, this paper proposes an adaptive FCM clustering algorithm DMGA-FCM based on a derivative multiple population genetic algorithm (DMGA). In DMGA-FCM algorithm, firstly, the derivative operator, which is proposed for the first time in this paper, performs derivative operations on initialized populations to improve the algorithm's searchability and deal with the lack of inter-population search ability. Secondly, the adaptive probability fuzzy control operator is used to dynamically adjust the genetic probability to improve the adaptability of the algorithm, which in turn enhances the global merit-seeking ability of the DMGA algorithm and avoids premature convergence. Finally, the initial clustering center of FCM algorithm is optimized with DMGA to enhance the clustering effect of the algorithm. The analysis of simulation experiments and MRI brain map application results show that the DMGA-FCM algorithm can obtain a better clustering effect of medical data and image clustering segmentation effect compared with other related FCM algorithms.

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Abbreviations

SAFCM:

Self-adaptive fuzzy C-means

CF:

Cluster forests

BOA:

Bayesian optimization algorithm

GSA:

Gravitational search algorithm

CSA:

Crow search optimization algorithm

CFCSA:

Chaos theory and fuzzy C-means algorithm

SSOA:

Social spider optimizer algorithm

NSGA:

Non-dominated sorting genetic algorithm

MAFC:

Fuzzy clustering based on multiple kernels

DMGA:

Derived multi-population genetic algorithm

MPGA:

Multi-population genetic algorithm

PSO:

Particle swarm optimization

BBO:

Biogeography based algorithm

FA:

Firefly algorithm

FFCM:

Fast fuzzy C-means

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 61300167 and 61976120, in part by the Natural Science Foundation of Jiangsu Province under Grant BK20191445, and in part by the Natural Science Key Foundation of Jiangsu Education Department under Grant 21KJA510004.

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Authors and Affiliations

  1. School of Information Science and Technology, Nantong University, Nantong, 226019, China

    Wei** Ding & Zhihao Feng

  2. Center for Computational Intelligence, School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK

    Javier Andreu-Perez

  3. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, T6R 2V4, Canada

    Witold Pedrycz

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  1. Wei** Ding
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  2. Zhihao Feng
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  4. Witold Pedrycz
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Correspondence to Wei** Ding.

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Ding, W., Feng, Z., Andreu-Perez, J. et al. Derived Multi-population Genetic Algorithm for Adaptive Fuzzy C-Means Clustering. Neural Process Lett 55, 2023–2047 (2023). https://doi.org/10.1007/s11063-022-10876-9

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