Improved Cuckoo Search Clustering Algorithm (ICSCA)

Abstract

Clustering is a division of data into groups of similar objects. Each group called a cluster consists of objects that are similar between themselves and dissimilar compared to objects of the other groups. Cuckoo Search Clustering Algorithm (CSCA) is a recently developed nature inspired, unsupervised classification method, based on the most recent meta-heuristic algorithm, stirred by the breeding strategy of the parasitic bird, the cuckoo. To better exploit the search space and to enhance the accuracy of this algorithm, an Improved Cuckoo Search Clustering Algorithm (ICSCA) is proposed in this paper. Normally, in the search space, a substantial fraction of the new solutions should be generated by far field randomization and whose locations should be far enough from the current best solution, this will make sure the system will not be trapped in a local optimum. This ICSCA algorithm that is expected to find the global cuckoo solution and exploit the search space more thoroughly than CSCA algorithm is proposed.