Research Article
A Neighborhood-Impact Based Community Detection Algorithm via Discrete PSO
Algorithm 1
Framework of the proposed algorithm.
(1) Input: | Adjacency matrix ; | Population size: pop; | Maximum generation: maxgen; | Tuning parameter: ; | (2) Step : Initialization | (1.1) Position initialization: ; | (1.2) Velocity initialization: ; | (1.3) Personal best position initialization: , ; | (1.4) Global best position initialization: select the global best position in , . | (3) Step : Iteration | (2.1) Calculate new velocity according to (11), ; | (2.2) Calculate new position according to (14), (15), and (16) | ; | if | Proposed strategy is adopted | else | Strategy in [25] is adopted | endif | (2.3) Function evaluation; | (2.4) Update personal best position ; | (2.5) Update global best position . | (4) Step : Termination Criteria | if maxgen is arrived | Stop and output ; | else | Go back to Step ; | end if | (5) Output: gbest. |
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