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Top1. Introduction1
Image segmentation is a technique of separating non-overlapping regions of an image. Since the last decade, researchers are using image segmentation as a preprocessing step of computer vision and image analysis. Therefore, the accuracy of segmentation is a vital factor for higher level image analysis. A lot of methods like thresholding based, edge based, region based, graph-cut based and connectivity-preserving relaxation methods for segmentation of image objects have been evolved till date. However, most of the researchers use thresholding-based segmentation in different applications because of its simplicity, accuracy, and robustness. In bi-level thresholding, image is divided into only two different homogeneous regions by using a single optimized threshold value whereas multi-level thresholding (Yin 1999, Yin 2007, Sathya et. al.2010, Hammouche 2008) divides the image into multiple regions by using the multiple optimized threshold values. Thresholding technique can be categorized into local and global, among the various global thresholding technique. There exist a number of techniques to compute thresholds. Otsu’s between class variance (Otsu, 1979) and Kapur’s entropy (Kapur et al.1985) are two popular of them which are used for image segmentation. The main challenge in any thresholding-based image segmentation is to find out the best set of thresholds for which the partitioning of different regions of an image may provide the best accuracy. Since the performance of classical optimization techniques broadly depend on the nature of the problems and type of objective functions, their efficiency largely depends on the solution space and the number of variables.
To overcome these drawbacks nature-inspired metaheuristic algorithms have become a research interest of many researchers in the recent days. For example, genetic algorithm (GA) (Yin, 1999; Hammouche, 2008), improved GA (Zhang et al., 2014), biography based optimization (BBO) (Simon, 2008), particle swarm optimization (PSO) (Yin, 2007; Kennedy et al., 1995; Shi, 2001), artificial bee colony (ABC) (Akay, 2013), modified ABC (Bhandari et al., 2015), differential evolution (DE) (Gandomi et al., 2012), bacterial foraging optimization (BFO) (Sathya et al., 2010), ant colony optimization(ACO) (Tao et al., 2007), cuckoo search (CS) (Agrawal et al., 2013), honey bee mating optimization (HBMO) (Horng, 2010), social spider optimization (SSO) (Ouadfel et al., 2016), flower pollination (Ouadfel et al., 2016), BAT (Yang, 2010) algorithm etc. have been developed and successfully applied to solve many optimization problems in different fields. But, in line with the no-free-lunch theorem (Wolpert et al., 1997), a metaheuristic algorithm may not always be able to produce the best result in solving all types of optimization problems. Therefore, we need to continually search a better metaheuristic algorithm which can be able to provide better results in solving a particular problem.