A Genetic Simulated Annealing Algorithm to Optimize the Small-World Network Generating Process

Algorithm 1

GSA algorithm.

1. Input: The initial network: G; The number of added edges: k; The size of population: S_pop; The mutation rate: P_m;

The number of iteration: I_max; The initial annealing temperature: T; The cooling coefficient of temperature: DELTA.

2. Encrypt unconnected edges of G, then get edges_encryption;

3. Initialize population with edges_encryption, i = 1;

4. Repeat;

5. Calculate score of population using SW by evaluate population;

6. Execute operation of natural selection to population according to score, 5% population were selected by seed selection

and 95% population were selected by roulette selection;

7. Execute operation of mutation to population;

8. T = T DELTA, i += 1;

9. If i < I_max, turn to 4, else end;

10. Output: the group of added edges, the optimized network.