Authors: Lu Xiong; Kangshun Li; Lei Yang
Addresses: College of Mathematics ad Informatics, South China Agricultural University, Guangzhou, GuangDong, China; Department of Computer Science, Guangdong University of Science and Technology, Dongguan, GuangDong, China ' College of Mathematics ad Informatics, South China Agricultural University, Guangzhou, GuangDong, China ' College of Mathematics ad Informatics, South China Agricultural University, Guangzhou, GuangDong, China
Abstract: We propose a complex network community discovery method based on parallel immune genetic algorithm for the problem of low efficiency, slow convergence rate and population degradation in community mining method based on genetic algorithm. The algorithm is making use of the principle of parallel immune system to ensure the diversity of population, and enhancing the searching ability by using a single path crossover operator in the initial population and crossover operation, in the initial population and crossover operation to enhance the ability to find the best use of the single path crossover operator, while using the improved character encoding and adaptive mutation operator to further reduce the search space, and improve the population degradation phenomenon. Experiments show that the improved parallel immune genetic algorithm is used to find the problem of complex network community with high accuracy, effectiveness and efficiency.
Keywords: genetic algorithm; community discovery; parallel immune; data mining.
International Journal of High Performance Computing and Networking, 2018 Vol.11 No.3, pp.242 - 250
Available online: 10 May 2018 *Full-text access for editors Access for subscribers Purchase this article Comment on this article