| Input: Adjacent matrix of network |
| Parameters: population size (popsize), max generations (gen), crossover probability (pc), mutation probability (pm) |
| Output: Pareto front solutions. |
| Step 1. Initialization |
| (1.1) Initialize the population with population initialization algorithm based on label propagation mechanism (Algorithm 2) |
| (1.2) Calculate individual objective functions KKM and RC with formula (3) |
| (1.3) Calculate the rank of each individual |
| If at least one objective value of individual is better than that of individual , |
| and all objects of are not worse than those of , then dominates . This is for each individual division level (rank), |
| and the rank of all non-dominant individuals is defined as 1, |
| and the other individual’s rank plus 1 with the number of individuals who control it. |
| (1.4) Calculate crowding distance |
| Calculate the distance between one individual and other individual in the same rank by |
| the crowding distance calculation method refers to [19]. |
| Step 2. Adopt the evolutionary strategy 6 to generate offspring individuals |
| Step 3. Pick out the dominant solutions of current generation from the population |
| The rank of all individuals is calculated first, |
| then select the individuals whose rank is 1 to construct dominant solutions of the current generation. |
| Step 4. Using the pruning mechanism to update the population |
| (4.1) Combine the dominant solutions with the present population to form a new population |
| (4.2) Calculate the rank of each individual and sort them from small to large. |
| (4.3) Select popsize individuals as the next generation according to the rank. |
| Step 5. Stopping criteria |
| If (iterations < gen), iterations ++ and go to Step , otherwise, stop the algorithm and output the dominant set of solutions. |
