# Skin Lesion Segmentation Method for Dermoscopy Images Using Artificial Bee Colony Algorithm
## 2 Materials and Methods  
The artificial bee colony algorithm is a swarm-based approach for optimization method, which replicates the smart foraging actions of honey bees [28]. A group of honey bees is called a swarm, which can effectively complete duties through a social teamwork. The artificial bee colony algorithm can be classified into three kinds of bees: The bees which food search near the food resources in their memory known as worker bees; these bees also communicate the knowledge of the food resources with the second kind of bees known as onlooker bees, which choose the high-quality food sources (fitness food resources). The scout bees are decoded from a small number of worker bees, which dispose of their food resources and look for fresh ones. The total swarm in the artificial bee colony algorithm is partitioned into two branches. The worker bees occupy the first half of the group of the bees' sections and the onlooker bees occupy the remaining section of the swarm. The whole solution of the swarm is equivalent to the number of bees (workers or onlookers) [28]. The random initial populations of the number of worker bees SR solutions of food resources are generated by the artificial bee colony, where SR refers to the number of the group of bees. Let \(Y_{i}=[Y_{i,1},Y_{i,2},...,Y_{i,N}]\) correspond to the \(i\)th solution in the group of bees, and \(N\) is the length size. Every worker bee \(Y_{i}\), which creates a new contestant resolution \(Z_{i}\) in the locality of its nearby position is represented by the following equation,  
\[Z_{i,j}=y_{i,j}+\sigma_{i,j}\cdot\left(y_{i,j}-y_{k,j}\right) \tag{1}\]  
where \(y_{k}\) is an arbitrarily elected contestant resolution (\(i\) not equal to \(k\)), j is an arbitrary length index selected from the group, and \(\sigma_{i,j}\) is an arbitrary number around (\(-1\) to \(1\)). A hungry selection is utilized when the new candidate resolution \(Z_{i}\) is generated. If the fitness value of \(Z_{i}\) is superior to that of its old \(Y_{i}\), then there is a revision of \(Y_{i}\) with \(Z_{i}\); otherwise the maintained \(y_{i}\) is fixed. After the search task has been completed by the all worker bees, they communicate the edible material information resources among the onlooker bees during the vibration motion. An onlooker bee receives this knowledge and calculates the nectar and after that selects an edible material resource beside the possibility associated to its quantity [29]. The probability selection (\(P_{i}\)) acts a roulette wheel selection (RWS) method, which is defined by the following equation,  
\[P_{i}=F_{i}/\sum_{j=1}^{SR}F_{i} \tag{2}\]where \(F_{i}\) means that fitness amount for \(i\)th resolution within the group like observed, \(i\) is a superior resolution, and a greater possibility for \(i\)th an edible material resource being preferred. When the location is not enhanced more than a limited number of iterations, then the edible material resource is discarded. Suppose that the discarded source is \(Y_{i}\), so the scout bee determines a fresh edible material resource to reinstate with \(Y_{i}\) by following equation,  
\[y_{i,j}=LB_{i,j}+r\big{(}UB_{i,j}-LB_{i,j}\big{)} \tag{3}\]  
While \(r\) is an arbitrary number around (0 to 1) founded on a normal distribution then, \(LB\) the minimum and \(UB\) the maximum limits are the \(j\)th length size, correspondingly [30; 31]. The steps in the artificial bee colony Algorithm 1 can be explained as:  
```
1:Arbitrarily initialize the group of bees. 2:Update the solution for the initial swarm. 3:for every worker bee, a new candidate resolution \(Z_{i}\) is produced based on Equation (1). Calculate the fitness of \(Z_{i}\) and a hunger selection is utilized to select a superior one between \(Y_{i}\) and \(Z_{i}\) as the new \(Y_{i}\). 4:Every onlook bee evaluates \(P_{i}\) based on Equation (2). 5:Produce a new candidate resolution \(Z_{i}\) based on Equation (1) and the present solution (food resource).