Hybrid fuzzy clustering methods based on improved self-adaptive cellular genetic algorithm and optimal-selection-based fuzzy c-means

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.