| | Input: |
| | , number of the VMs; |
| | , number of the physical servers; |
| | , population size; |
| | D, dimension of particles |
| | , maximum size of ; |
| | , maximum number of iteration; |
| | , , minimum and maximum bounds of random number. |
| | Output: E: the archive set (Pareto set) of the optimal VM placement solutions. |
| 01: Set t = 0, , , , i = (1, 2, …, R), d = (1, 2, …, D), |
| 02: Repeat |
| 03: for i = 1 to R do |
| 04: Generate a random position for the ith particle; |
| 05: Evaluate the fitness value of for all i using formulas (6), (7), and (8); |
| 06: / External archive updating / |
| 07: if≻then |
| 08: Update : ; |
| 09: else if ()⋀() then |
| 10: Update ; |
| 11: end if |
| 12: if then |
| 13: fordo |
| 14: Calculate the crowding entropy of each solution in E according to formula (17); |
| 15: Sort archive in descending values; |
| 16: Update , is the removed solution (redundant solutions); |
| 17: Select the global best position randomly from the top of |
| 18: end for |
| 19: end if |
| 20: Personal best position update; |
| 21: end for |
| 22: for i = 1 to R do |
| 23: for d = 1 to D do |
| 24: Update the velocity using formula (12); |
| 25: Compute the new position using formula (13) |
| 26: end for |
| 27: end for |
| 28: t = t + 1;//increment steps |
| 29: Until |
| 30: Output archive E (the Pareto set) |
