| Input: Adjacent matrix of network |
| Parameters: population size (popsize), max generations (gen), crossover probability (pc), |
| mutation probability (pm), initial probability of adaptive strategy (p) |
| Output: Optimal solution of the current iteration |
| Step 1. Initialization |
| (1.1) Initialize each individual by label propagation mechanism (see Algorithm 2). |
| (1.2) Calculate objective function using formula (1). |
| Step 2. Self-adaptive learning |
| For each individual, select a strategy from hybrid evolutionary strategy pool (see Section 3.1.3) |
| using roulette wheel selection according to the selected probability, |
| then update the selected probability of each strategy by self-adaptive learning framework (see Section 3.1.4). |
| Step 3. Local search |
| Apply hill-climbing search (see Section 3.1.5) to the individual with highest value in the current population for local search. |
| Once a better individual is generated, the new individual will replace the chosen one. |
| Repeat until no more better individual is get or the number of search reaches the maximum, |
| then the individual is the current best solution of the population. |
| Step 4. Stopping criteria: |
| If (iterations < gen), iterations ++, and go to Step ; otherwise, stop the algorithm and output. |
