Clustering with a Novel Global Harmony Search Algorithm for Image Segmentation

Abstract

A state-feedback based harmony search (SFHS) algorithm to clustering data is presented to solve the problems that the traditional evolutionary-based clustering algorithms easily trap into the local optimum. Firstly, the state-feedback mechanism is introduced into HS algorithm, and the harmony memory difference metric is defined to adaptively adjust harmony memory considering rate and pitch adjusting bandwidth, which makes the convergence and efficiency of the harmony search (HS) algorithm improved obviously. Secondly, in the SFHS-based clustering algorithm, the decision variables in the harmony vector represent cluster centers, and the harmony vector represents a partition of data, the best partition of data can be obtained by updating the harmony memory. Finally, a novel validity metric is presented to determine the right number of clusters. Simulation experiments have been carried out on remote sensing images and animal images, and the relevant results are compared with the ANT-based clustering algorithm and the traditional HS-based clustering algorithms, it shows that the SFHS-based clustering algorithm has better convergence.