【摘要】：針對大部分基于智能優(yōu)化算法的社區(qū)發(fā)現(xiàn)方法存在的種群退化、尋優(yōu)能力不強、計算過程復雜、需要先驗知識等問題,提出了一種基于免疫遺傳算法(GA)的復雜網(wǎng)絡社區(qū)發(fā)現(xiàn)方法。算法將改進的字符編碼和相應的遺傳算子相結(jié)合,在不需要先驗知識的情況下可自動獲得最優(yōu)社區(qū)數(shù)和社區(qū)劃分方案;將免疫原理引入遺傳算法的選擇操作中,保持了群體多樣性,改善了遺傳算法所固有的退化現(xiàn)象;在初始化種群及交叉和變異算子中利用網(wǎng)絡拓撲結(jié)構(gòu)的局部信息,有效縮小了搜索空間,增強了尋優(yōu)能力。計算機生成網(wǎng)絡和真實網(wǎng)絡上的仿真實驗結(jié)果表明算法可自動獲取最優(yōu)社區(qū)數(shù)和社區(qū)劃分方案并具有較高的精度,說明算法具有可行性和有效性。
[Abstract]:In order to solve the problems of population degradation, poor optimization ability, complex calculation process and the need of prior knowledge in most community discovery methods based on intelligent optimization algorithms, etc. A complex network community discovery method based on immune genetic algorithm (GA) is proposed. By combining the improved character encoding with the corresponding genetic operator, the optimal community number and community partition scheme can be obtained automatically without prior knowledge, and the immune principle is introduced into the selection operation of genetic algorithm. The diversity of population is preserved and the inherent degradation of genetic algorithm is improved. The local information of network topology is used in initializing population and crossover and mutation operator to effectively reduce the search space and enhance the searching ability. The simulation results on the computer generated network and real network show that the algorithm can automatically obtain the optimal community number and community partition scheme, and has a high accuracy, which shows that the algorithm is feasible and effective.
【作者單位】： 西北民族大學數(shù)學與計算機科學學院;
【基金】：國家自然科學基金資助項目(11161041) 2012年度國家民委科研項目基金資助項目 中央高�；究蒲许椖炕鹳Y助項目(31920130009,zyz2012081)
【分類號】：TP18;TP393.02

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本文編號：2156720