Modified Remora Optimization Algorithm for Global Optimization and Multilevel Thresholding Image Segmentation

Qingxin Liu, Ni Li, Heming Jia, Qi Qi and Laith Abualigah
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Qingxin Liu: School of Computer Science and Technology, Hainan University, Haikou 570228, China
Ni Li: School of Mathematics and Statistics, Hainan Normal University, Haikou 571158, China
Heming Jia: School of Information Engineering, Sanming University, Sanming 365004, China
Qi Qi: School of Computer Science and Technology, Hainan University, Haikou 570228, China
Laith Abualigah: Faculty of Computer Sciences and Informatics, Amman Arab University, Amman 11953, Jordan

Mathematics, 2022, vol. 10, issue 7, 1-42

Abstract: Image segmentation is a key stage in image processing because it simplifies the representation of the image and facilitates subsequent analysis. The multi-level thresholding image segmentation technique is considered one of the most popular methods because it is efficient and straightforward. Many relative works use meta-heuristic algorithms (MAs) to determine threshold values, but they have issues such as poor convergence accuracy and stagnation into local optimal solutions. Therefore, to alleviate these shortcomings, in this paper, we present a modified remora optimization algorithm (MROA) for global optimization and image segmentation tasks. We used Brownian motion to promote the exploration ability of ROA and provide a greater opportunity to find the optimal solution. Second, lens opposition-based learning is introduced to enhance the ability of search agents to jump out of the local optimal solution. To substantiate the performance of MROA, we first used 23 benchmark functions to evaluate the performance. We compared it with seven well-known algorithms regarding optimization accuracy, convergence speed, and significant difference. Subsequently, we tested the segmentation quality of MORA on eight grayscale images with cross-entropy as the objective function. The experimental metrics include peak signal-to-noise ratio ( PSNR ), structure similarity ( SSIM ), and feature similarity ( FSIM ). A series of experimental results have proved that the MROA has significant advantages among the compared algorithms. Consequently, the proposed MROA is a promising method for global optimization problems and image segmentation.

Keywords: remora optimization algorithm; multi-level thresholding image segmentation; cross-entropy; meta-heuristic; optimization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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