Energy Efficiency Opposition-Based Learning and Brain Storm Optimization for VNF-SC Deployment in IoT

Algorithm 2

VNF-SC mapping algorithm-based OBLBSO.

1 individuals are randomly generated;

2 The individuals were clustered in the decision space and divided into clusters;

3 Evaluate individual fitness value;

4 The individuals in each cluster were sorted and the best individuals in each cluster were recorded as the center of the cluster;

5 A random number between 0 and 1 is generated randomly. If the random number is less than the preset probability of , an individual will be generated randomly to replace a cluster center;

6 Randomly generate a number between 0 and 1;

7 if is less than the default probability of then

8 Random into a random number between 0 and 1, and randomly choose a cluster;

9 If the random number is less than , the center of the cluster is selected and a new individual is generated through Gaussian variation;

10 Otherwise, other individuals in the cluster are selected and new individuals are generated by Gaussian variation;

11 else

12 Randomly generate a number between 0 and 1;

13 If the random number is less than , then a new individual is generated based on the center of the two clusters through Gaussian variation. Otherwise, two individuals selected at random based on two clusters will be generated by Gaussian variation;

14 end

15 The newly generated individuals were compared with the existing ones, and the well-preserved individuals were taken as the next generation of new individuals;

16 If a preset maximum number of iterations is reached, stop, or skip to Step 2.