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

Algorithm 2

The Monte Carlo method.

1. Input: The initial network: G; The size of population: S_pop; The mutation rate: P_m; The annealing temperature: T;

The cooling coefficient of temperature: DELTA.

2. Choose an new chromosome from S_pop;

3. Initialize an random number n in , if n < P_m, turn to 4; If n >= P_m, turn to 9;

4. Select a random integer from edges_encryption to replace one of numbers in the chromosome, thus produce

an mutated chromosome;

5. Calculate by add edges with original chromosome and get with mutated chromosome;

6. ;

7. If , replace original chromosome by mutated chromosome and turn to 9; If Δf < 0, turn to 8;

8. , initialize a random number m in . If m <, replace original chromosome by mutated chromosome;

9. If all the number in the original chromosome have been selected then turn to 2, else turn to 3;

10. End.