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

This paper proposes an improved form of the ant lion optimization algorithm (IALO) to solve image clusteringproblem. The improvement of the algorithm was made using a new boundary decreasing procedure. Moreover, a recentlyproposed 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 Kmeans 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.

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