Multiobjective Brain Storm Optimization Community Detection Method Based on Novelty Search
Algorithm 2
MOBSO-NS.
Input: the edge set data of complex network, the maximum number of iterations of the algorithm maxIter, the population size popNum, and the proportion of the three new individuals generating the new population are different α, β, γ (α + β + γ = 1); Q is the maximum number of times that the partial archive has not been updated, and P is the maximum number of iterations restarted.
Output: a group of network communities are divided into structures.
(1)
Initialize: Pops = ∅, EP = ∅
(2)
For i = 1 to popNum do:
(3)
The LAR code is used to randomly generate an initial solution s, as shown in Figure 4, and the NRA and RC values for s are calculated according to formula (2).
(4)
Add s to Pops.
(5)
End For
(6)
Iterative search:
(7)
For iter = 1 to maxIter do:
(8)
For s in popNum do:
(9)
If no solution in EP can dominate s do
(10)
Add s to EP, and remove all solutions in EP that can be dominated by s.
(11)
End If
(12)
End For
(13)
newPops = ∅。
(14)
Individual update
(15)
Restart
(16)
End For
(17)
Calculate the indicator value:
(18)
For s in EP do:
(19)
Calculate the Q value and NMI value of s。。
(20)
End For
(21)
Return the two solutions SI and S2 a maximum value of 0 and NMI in EP
