Research Article

Hybrid Self-Adaptive Algorithm for Community Detection in Complex Networks

Algorithm 1

The flow of HSCDA.
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.