Improved COA with Chaotic Initialization and Intelligent Migration for Data Clustering

K-means algorithm is a well-known clustering algorithm. In spite of its advantages such as high speed and ease of employment, this algorithm suffers from the problem of local optima. In order to overcome this problem, a lot of works have been carried out on clustering. This paper presents a hybrid extended cuckoo optimization algorithm (ECOA) and K-means (K) algorithm called ECOA-K. The COA algorithm has advantages such as fast convergence rate, intelligent operators, and a simultaneous local and global search work, which are the motivations behind choosing this algorithm. In ECOA, we have enhanced the operators in the classical version of the cuckoo algorithm. The proposed operator for production of the initial population is based upon a chaos sequence, whereas in the classical version, it is based upon a randomized series. Moreover, allocating the number of eggs to each cuckoo in the revised algorithm is done based on its fitness. Another improvement is in the cuckoos’ migration, which is performed with different deviation degrees. The proposed method is evaluated on several standard datasets at the UCI database, and its performance is compared with those of black hole (BH), big bang big crunch (BBBC), cuckoo search algorithm (CSA), traditional cuckoo optimization algorithm (COA), and K-means algorithm. The results obtained are compared in terms of the purity degree, coefficient of variance, convergence rate, and time complexity. The simulation results show that the proposed algorithm is capable of yielding the optimized solution with a higher purity degree, faster convergence rate, and stability, in comparison with the other algorithms.