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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/23336
Title: Opposition-based Laplacian Equilibrium Optimizer with application in Image Segmentation using Multilevel Thresholding
Authors: Dinkar S.K.
Deep, K.
Mirjalili S.
Thapliyal S.
Published in: Expert Systems with Applications
Abstract: This paper proposes a modified version of freshly developed Equilibrium Optimizer (EO) for segmentation of gray-scale images using multi-level thresholding. Laplace distribution based random walk is utilized to update the concentration of search agents around equilibrium candidates (best solution) towards to attain optimal position (equilibrium state) for achieving better diversification of search space. An opposition based learning (OBL) mechanism is then applied with hybridization of the varying acceleration coefficient to the best solution for accelerating exploitation at a later phase of each iteration. The performance of proposed Opposition-based Laplacian Equilibrium Optimizer (OB-L-EO) is validated using test suites containing benchmark problems of wide varieties of complexities. Various analyses are conducted including Wilcoxon ranksum test for statistical significance, convergence curves and distance between solution before and after applying modification strategies. Finally, the proposed OB-L-EO is employed for image segmentation by utilizing Otsu's interclass variance function to obtain optimum threshold values for image segmentation. The performance of the proposed algorithm is verified by determining mean value of interclass variance and peak signal to noise ratio (PSNR). The obtained results are then compared and analysed with other metaheuristics algorithms to show superiority of proposed OB-L-EO. © 2021 Elsevier Ltd
Citation: Expert Systems with Applications, 174
URI: https://doi.org/10.1016/j.eswa.2021.114766
http://repository.iitr.ac.in/handle/123456789/23336
Issue Date: 2021
Publisher: Elsevier Ltd
Keywords: Equilibrium Optimizer
Image segmentation
Meta-heuristics
Opposition-based learning
Optimization
ISSN: 9574174
Author Scopus IDs: 57196220500
8561208900
51461922300
57222273707
Author Affiliations: Dinkar, S.K., Department of Computer Science and Applications, GBPIET, Pauri GarhwalUttarakhand, India
Deep, K., Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
Mirjalili, S., Centre for Artificial Intelligence Research and Optimization, Torrens University Australia, Brisbane, QLD 4006, Australia, YFL (Yonsei Frontier Lab), Yonsei University, Seoul, South Korea
Thapliyal, S., Department of Computer Science and Applications, GBPIET, Pauri GarhwalUttarakhand, India
Corresponding Author: Dinkar, S.K.; Department of Computer Science and Applications, Pauri Garhwal, India
Appears in Collections:Journal Publications [MA]

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