The Lyapunov Optimization for Two-Tier Hierarchical-Based MAC in Cloud Robotics

Algorithm 1

GA algorithm.

Input: size of the population T, maximum generation G, crossover probability p_c, mutation probability p_m.

Output:

1 Initialization

2 Randomly initialize sets T of optimization’s variables as the initial population with constrains C2 and C3’

3 Coding into by function , i.e.,

4 Whiledo

5 Calculate the individual fitness according

6 Calculate the selection probability

7 Save the best fitness and the corresponding individual

8 If, then

9 Return

10 End

11 Selection: randomly choose T chromosomes as a new population by Roulette Wheel selection

12 Crossover: for every two pair of individuals in , take multi-point crossover at every gene position with probability

13 Mutation: for every individual in , take binary-reverse at every gene position with the probability

14 

15 

16 End

17 Return