International Journal of Electrical and Computer Engineering (IJECE) Vol. 11, No. 6, December 2021, pp. 5420~5429 ISSN: 2088-8708, DOI: 10.11591/ijece.v11i6.pp5420-5429  5420 Journal homepage: http://ijece.iaescore.com Image multi-level-thresholding with Mayfly optimization Seifedine Kadry 1 , Venkatesan Rajinikanth 2 , Jamin Koo 3 , Byeong-Gwon Kang 4 1 Department of Mathematics and Computer Science, Faculty of Science, Beirut Arab University, Lebanon 2 Department of Electronics and Instrumentation Engineering, St. Joseph’s College of Engineering, Chennai, India 3,4 Department of Information and Communication Technology (ICT) Convergence, Soonchunhyang University, Asan, South Korea Article Info ABSTRACT Article history: Received Dec 4, 2020 Revised Apr 5, 2021 Accepted Apr 26, 2021 Image thresholding is a well approved pre-processing methodology and enhancing the image information based on a chosen threshold is always preferred. This research implements the mayfly optimization algorithm (MOA) based image multi-level-thresholding on a class of benchmark images of dimension 512x512x1. The MOA is a novel methodology with the algorithm phases, such as; i) Initialization, ii) Exploration with male-mayfly (MM), iii) Exploration with female-mayfly (FM), iv) Offspring generation and, v) Termination. This algorithm implements a strict two-step search procedure, in which every Mayfly is forced to attain the global best solution. The proposed research considers the threshold value from 2 to 5 and the superiority of the result is confirmed by computing the essential Image quality measures (IQM). The performance of MOA is also compared and validated against the other procedures, such as particle-swarm-optimization (PSO), bacterial foraging optimization (BFO), firefly-algorithm (FA), bat algorithm (BA), cuckoo search (CS) and moth-flame optimization (MFO) and the attained p-value of Wilcoxon rank test confirmed the superiority of the MOA compared with other algorithms considered in this work. Keywords: Feature-similarity-index Wilcoxon test Mayfly optimization Otsu Thresholding This is an open access article under the CC BY-SA license. Corresponding Author: Byeong-Gwon Kang Department of ICT Convergence Soonchunhyang University Asan 31538, South Korea Email: bgkang@sch.ac.kr 1. INTRODUCTION Recently, a considerable number of research works are proposed and implemented by the researches in various domains, in which the images recorded using a chosen procedure commonly; help to asses the essential information [1]-[5]. Image processing is emerged as one of the key research domains and widely adopted to process red, green, dan blue (RGB)/grayscale images with chosen methodologies [6]-[8]. Even though a considerable number of image processing approaches exist, the image multi-level thresholding (IMLT) is emerged as one of the vital methods to process various class images with assigned threshold values [9]-[12]. In most of the cases, the IMLT is considered to enhance the image texture/pixel information by grouping alike pixels based on the chosen thresholds. It can be implemented with recommended methods, such as Otsu, Kapur, Shannon and Tsallis with common values of preferred thresholds (T) as; 2-5 [13]-[18]. From year 1979, Otsu is commonly employed to enhance the RGB/grey scale images based on the chosen threshold [19]. Due to its superiority, this approach is widely adopted to pre-process a class of medical images recorded with different modalities [20], [21]. The earlier works in literature also confirm the