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Improved Grey Wolf Optimizer based on Levy Flight for Multi-thresholding Image Segmentation

by Ntaye Emmanuel, Michael Asante, Dennis Redeemer Korda, Emmanuel Oteng Dapaah, Dickson Kodzo Mawuli Hodowu
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 184 - Number 49
Year of Publication: 2023
Authors: Ntaye Emmanuel, Michael Asante, Dennis Redeemer Korda, Emmanuel Oteng Dapaah, Dickson Kodzo Mawuli Hodowu
10.5120/ijca2023922595

Ntaye Emmanuel, Michael Asante, Dennis Redeemer Korda, Emmanuel Oteng Dapaah, Dickson Kodzo Mawuli Hodowu . Improved Grey Wolf Optimizer based on Levy Flight for Multi-thresholding Image Segmentation. International Journal of Computer Applications. 184, 49 ( Mar 2023), 1-12. DOI=10.5120/ijca2023922595

@article{ 10.5120/ijca2023922595,
author = { Ntaye Emmanuel, Michael Asante, Dennis Redeemer Korda, Emmanuel Oteng Dapaah, Dickson Kodzo Mawuli Hodowu },
title = { Improved Grey Wolf Optimizer based on Levy Flight for Multi-thresholding Image Segmentation },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2023 },
volume = { 184 },
number = { 49 },
month = { Mar },
year = { 2023 },
issn = { 0975-8887 },
pages = { 1-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume184/number49/32632-2023922595/ },
doi = { 10.5120/ijca2023922595 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:24:20.583938+05:30
%A Ntaye Emmanuel
%A Michael Asante
%A Dennis Redeemer Korda
%A Emmanuel Oteng Dapaah
%A Dickson Kodzo Mawuli Hodowu
%T Improved Grey Wolf Optimizer based on Levy Flight for Multi-thresholding Image Segmentation
%J International Journal of Computer Applications
%@ 0975-8887
%V 184
%N 49
%P 1-12
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The Gray Wolf Optimizer is a relatively new and efficient population-based optimizer that seeks to speed up computations and find optimal solution for image segmentation problems. It is a metaheuristic algorithm that mimics the social hierarchy and hunting behavour of the gray wolfs. However, because of the insufficient diversity wolves in some cases, it is still prone to stagnation at a local optimum. This may often happen when the GWO is not able to perform a smooth transaction from exploration to exploitation potential by more iteration. This paper proposed an improved gray wolf optimizer for Multilevel image segmentation based on levy flight (LGWO). Levy flight is an efficient strategy that increase the population diversity and prevents premature convergence by improving the ability to jump out of a local optimum. The performance of the LGWO is than evaluated and compared with two conventional population-based algorithms, the Particle Swarm Optimizer (PSO) and the Bat Algorithm (BA) by using the Kapur’s entropy and Otsu’s between-class variance function with ten standard gray scale images in a multi-threshold problem. The quality of the segmented images is compared using the maximum objective function, peak signal- to noise ratio (PSNR), CPU computation time and the optimal threshold value. The experimental results proved the LGWO algorithm an efficient and reliable algorithm in solving continuous image segmentation problems.

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Index Terms

Computer Science
Information Sciences

Keywords

Segmentation Gray Wolf Optimizer Optimization Lévy Flight

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