Enhanced Butterfly Optimization Based Clustering for Digital Images

Authors

  • Janani Priya Mohan , Yamuna Govindarajan

Keywords:

Meta-heuristic algorithms, image segmentation, fuzzy based clustering, butterfly optimization

Abstract

Butterfly Optimization Algorithm (BOA) is a metaheuristic optimization algorithm mathematically modelling the foraging behaviors of butterflies for solving optimization problems. This article first modifies the BOA by incorporating sudden and fast movements of butterflies for escaping from predators. Segmentation of digital images is a vital process in classification, object recognition and other computer vision applications.  Clustering is a class of segmentation that can relate one pixel to many classes of the given digital image. This article formulates such clustering method as an optimization problem suitable for metaheuristic environment by defining the butterfly to represent cluster centroids and developing a fitness function from the cost function of the classical fuzzy method (CFM). It then applies the enhanced BOA (EBOA) for obtaining optimal centroids. This article presents the results of the proposed method on six digital images and compares its performance with the CFM.

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Published

24.03.2024

How to Cite

Yamuna Govindarajan , J. P. M. , . (2024). Enhanced Butterfly Optimization Based Clustering for Digital Images. International Journal of Intelligent Systems and Applications in Engineering, 12(3), 2069–2074. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5674

Issue

Vol. 12 No. 3 (2024)

Section

Research Article

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