An improved form of the ant lion optimization algorithm for image clustering problems

Meti̇n Toz

doi:10.3906/elk-1703-240

Abstract

Thispaperproposesanimprovedformoftheantlionoptimizationalgorithm(IALO)tosolveimageclustering problem. The improvement of the algorithm was made using a new boundary decreasing procedure. Moreover, a recently proposed objective function for image clustering in the literature was also improved to obtain well-separated clusters while minimizing the intracluster distances. In order to accurately demonstrate the performances of the proposed methods, firstly, twenty-three benchmark functions were solved with IALO and the results were compared with the ALO and a chaos-based ALO algorithm from the literature. Secondly, four benchmark images were clustered by IALO and the obtained results were compared with the results of particle swarm optimization, artificial bee colony, genetic, and K- means algorithms. Lastly, IALO, ALO, and the chaos-based ALO algorithm were compared in terms of image clustering by using the proposed objective function for three benchmark images. The comparison was made for the objective function values, the separateness and compactness properties of the clusters and also for two clustering indexes Davies– Bouldin and Xie–Beni. The results showed that the proposed boundary decreasing procedure increased the performance of the IALO algorithm, and also the IALO algorithm with the proposed objective function obtained very competitive results in terms of image clustering.

Highlights

Clustering is an unsupervised data grouping technique that has been widely applied in many fields such as machine learning, pattern recognition, data mining, and image processing [1]
The rest of the paper is organized as follows; the Ant lion optimization (ALO) algorithm and the proposed IALO algorithm are presented in details in Section 2, the solution method of the image clustering problems by using the IALO algorithm and the proposed objective function is given in Section 3, the experiments and the comparisons are presented in Section 4, and the paper concluded with the last section
These three images are used to test the performance of the standard ALO, IALO, and the chaos-based ALO (CALO) algorithms on solving image clustering problem by the proposed objective function in this study

Summary

Introduction

Clustering is an unsupervised data grouping technique that has been widely applied in many fields such as machine learning, pattern recognition, data mining, and image processing [1]. K-means clustering algorithm that was proposed by McQueen [5] and its fuzzy-based version, the fuzzy C-means (FCM) proposed by Dunn [6] and improved by Bezdek [7], are the two most widely known partitional algorithms These two algorithms have very simple formulations to be applied to most kinds of the clustering applications and very computational efficient. Karaboga and Ozturk [19] proposed to use the ABC algorithm for clustering applications and showed that the ABC algorithm outperforms PSO and nine classification techniques from the literature in solving data clustering problem. In [21], Zawbaa et al proposed to use an improved form of ALO by chaos (CALO) to solve feature selection problem in data mining They formulated the problem as a multiobjective optimization problem and tested the proposed algorithm on different datasets. The rest of the paper is organized as follows; the ALO algorithm and the proposed IALO algorithm are presented in details in Section 2, the solution method of the image clustering problems by using the IALO algorithm and the proposed objective function is given in Section 3, the experiments and the comparisons are presented in Section 4, and the paper concluded with the last section

Ant lion optimization algorithm

Improved form of the ALO algorithm

Conclusion