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Complex network community detection based on
multiobjective adaptive Memetic algorithm |
| School of Computer Science and Technology, Tiangong University, Tianjin 300387, China |
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Abstract To improve the accuracy of complex network community detection, a multiobjective complex network community detection algorithm was proposed based on the adaptive memetic algorithm. According to the Randomwalker initialization strategy, the Logistic function was combined with the fitness function, and the dynamic adaptive strategy was introduced to adjust the crossover and mutation probability. The network topology was also mined，and the community detection accuracy was improved. The multiobjective optimization was transformed into two functions of the minimum optimization connectivity (MRA) and the segmentation (RC). In the local searching, the two objective functions were used to form a local optimization target by the weighted sum method, and the hill climbing algorithm was used to find individual optimal. The algorithm was verified on the artificial and real datasets. The results show that the proposed algorithm can effectively improve the accuracy of community detection with good optimization effect.
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