A Clustering Based Classification Approach Based on Modified Cuckoo Search Algorithm

Abstract

Cuckoo Search Algorithm (CSA) is one of the new swarm intelligence based optimization algorithms, which has shown an effective performance on many optimization problems. However, the effectiveness of CSA significantly depends on the exploration and exploitation potential and it may also possible to increase its efficiency when solving complex optimization problems. In this study, some mechanisms have been employed on CSA to increase its efficiency such as use of global best and individual best solutions to guide the other solutions, self-adaption techniques for parameters and so on. The modified CSA (i.e., MCSA) is successfully employed in clustering based classification domain. The experimental results and execution time prove its effectiveness over existing modified CSAs and other employed swarm intelligence algorithms. The proposed clustering model is also employed in color histopathological image segmentation domain and provides effective result.