An improved artificial bee colony algorithm based on mean best-guided approach for continuous optimization problems and real brain MRI images segmentation

Spatial information of fuzzy clustering based mean best artificial bee colony algorithm for phantom brain image segmentation

“…Where (i = 1,…, SN) and SN are indicated to the solutions D of parameters value (j = 1,2, ..., D), while xj min and xj max are the upper and lower value of the dimension j, respectively. The value of the updated source of food is computed as in (8).…”

“…In Alrosan et al [8], [24] an improved new version of ABC that is called MeanABC, and it presents a new modified search equation for the kind of employ bee phase, which depends on the information about the best candidates to regenerate a new neighborhood source of food as in (11).…”

“…Because it has high performance in the computational process, effectiveness and straight forward, it is commonly used in the clustering of image. In contrast, it has serious shortcomings such as sensitivity to noise and stuck in local optimum [7], [8]. In order to solve these problems, evolutionary algorithms and variants such as firefly algorithm (FA) [9], [10], particle swarm optimization (PSO) [11], cuckoo search (CS) [12], [13], and artificial bee colony (ABC) [14] and harmony search (HS) [15]- [17] have been successes in many domains such as segmentation of images [1], [2], [8], [18]- [20], clustering [21] and in several fields [22], [23].…”

“…In contrast, it has serious shortcomings such as sensitivity to noise and stuck in local optimum [7], [8]. In order to solve these problems, evolutionary algorithms and variants such as firefly algorithm (FA) [9], [10], particle swarm optimization (PSO) [11], cuckoo search (CS) [12], [13], and artificial bee colony (ABC) [14] and harmony search (HS) [15]- [17] have been successes in many domains such as segmentation of images [1], [2], [8], [18]- [20], clustering [21] and in several fields [22], [23]. In this paper, several studies have been reviewed, which are related to image segmentation methods based clustering approach with evolutionary algorithms, these studies are reviewed as follow, In Alrosan et al [20], proposed fuzzy c-means were combined with ABC algorithms and called ABC-FCM, and this method was carried out by two kinds of MRI images namely the simulated brain MRI images, Alrosan et al [8], [24] presented a novel version of ABC algorithm, namely (Mean WBC) that is merged with the FCM clustering approach.…”

Smart Home Battery for the Multi-Objective Power Scheduling Problem in a Smart Home Using Grey Wolf Optimizer

“…Meta-heuristic optimization algorithms are the most popular algorithms adapted to address optimization problems in different fields [22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39]. A massive number of this kind of optimization algorithms were adapted to address PSPSH optimally and obtain an optimal schedule [14].…”