Decomposition-Based Multiobjective Evolutionary Algorithm for Community Detection in Dynamic Social Networks

Algorithm 2

Local search procedure.

Input:

 𝐷: The subpopulation before local search

 𝑆_𝐷: Size of 𝐷

 𝐾: Number of neighbors

Output:

 𝐷^′: The new subpopulation after local search

Step  1. Set 𝑖=1, 𝐷^′=∅.

Step  2. If 𝑖>𝑆_𝐷, the algorithm terminates. Export 𝐷^′ as the new population.

     Otherwise, select the 𝑖th individual in 𝐷, set 𝑘=1.

Step  3. If 𝑘>𝐾, the search procedure stops for the 𝑖th individual,

     go to Step  7. Otherwise, go to Step  4;

Step  4. Assume the 𝑗th gene need to do local search, attain all the neighbors of node 𝑗,

     find the label of community which most neighborhood nodes belong to.

     And then select one from these nodes to replace the 𝑗th gene

     by the corresponding value.

Step  5. Calculate the value of objective function of the new individual according to the

     corresponding single-objective sub problem. If its value is greater than

     that before local search, replace the current individual by the new one, go to Step  7,

     otherwise, go to Step  6.

Step  6. 𝑘=𝑘+1, go to Step  3.

Step  7. Add the current individual to 𝐷^′, 𝑖=𝑖+1, go to Step  2