Hybrid fuzzy clustering methods based on improved self-adaptive cellular genetic algorithm and optimal-selection-based fuzzy c-means
•The new dynamic crossover and entropy-based two-combination mutation operations are constructed to prevent the convergence of the algorithms to a local optimum by adaptively modifying the probabilities of crossover and mutation as well as mutation step size according to dynamic adjusting strategies...
Saved in:
| Published in: | Neurocomputing (Amsterdam) Vol. 249; pp. 140 - 156 |
|---|---|
| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier B.V
02.08.2017
|
| Subjects: | |
| ISSN: | 0925-2312, 1872-8286 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | •The new dynamic crossover and entropy-based two-combination mutation operations are constructed to prevent the convergence of the algorithms to a local optimum by adaptively modifying the probabilities of crossover and mutation as well as mutation step size according to dynamic adjusting strategies and judging criterions.•An improved self-adaptive cellular genetic algorithm (IDCGA) is presented for a more efficient search by combining the Arnold cat map with modified evolution rule, as well as the constructed dynamic crossover and the entropy based two-combination mutation operators.•Two novel adaptive fuzzy clustering algorithms based on IDCGA, referred to as IDCGA-FCM and IDCGA2-FCM, are proposed in this paper. The first one is a standalone form for fuzzy clustering on the basis of IDCGA. The second one is a hybrid method based on FCM and IDCGA which takes advantage of the merits of both algorithms.•The experimental results showed that the presented algorithms have high efficiency and accuracy.
With an aim to overcome low efficiency and improve the performance of fuzzy clustering, two novel fuzzy clustering algorithms based on improved self-adaptive cellular genetic algorithm (IDCGA) are proposed in this paper. The new dynamic crossover and entropy-based two-combination mutation operations are constructed to prevent the convergence of the algorithms to a local optimum by adaptively modifying the probabilities of crossover and mutation as well as mutation step size according to dynamic adjusting strategies and judging criterions. Arnold cat map is employed to initialize population for the purpose of overcoming the sensitivity of the algorithms to initial cluster centers. A modified evolution rule is introduced to build a dynamic environment so as to explore the search space more effectively. Then a new IDCGA that combined these three processes is used to optimize fuzzy c-means (FCM) clustering (IDCGA-FCM). Furthermore, an optimal-selection-based strategy is presented by the golden section method and then a hybrid fuzzy clustering method (IDCGA2-FCM) is developed by automatically integrating IDCGA with optimal-selection-based FCM according to the variation of population entropy. Experiments were performed with six synthetic datasets and seven real-world datasets to compare the performance of our IDCGA-based clustering algorithms with FCM, other GA-based and PSO-based clustering methods. The results showed that the presented algorithms have high efficiency and accuracy. |
|---|---|
| ISSN: | 0925-2312 1872-8286 |
| DOI: | 10.1016/j.neucom.2017.03.068 |