CAJ | 학술논문

针对使用遗传算法进行复杂网络社团发现时，存在较强随机性以及容易陷入局部最优解的缺陷，提出一种基于遗传框架的复杂网络社团发现新方法。其通过一次迭代标签传播方法进行种群初始化，针对字符串表示法交叉困难的特点提出了统一标签交叉策略，并采用有指向性的变异策略解决遗传算法随机变异的缺陷问题。实验结果表明：对典型的人工生成网络结构和真实网络结构，该方法能够较准确地发现社团结构；与经典算法进行比较，该方法具有较高的社团发现精度且收敛速度较快。
침대사용유전산법진행복잡망락사단발현시，존재교강수궤성이급용역함입국부최우해적결함，제출일충기우유전광가적복잡망락사단발현신방법。기통과일차질대표첨전파방법진행충군초시화，침대자부천표시법교차곤난적특점제출료통일표첨교차책략，병채용유지향성적변이책략해결유전산법수궤변이적결함문제。실험결과표명：대전형적인공생성망락결구화진실망락결구，해방법능구교준학지발현사단결구；여경전산법진행비교，해방법구유교고적사단발현정도차수렴속도교쾌。
Against the defects of stronger randomness and local optimal solution when the community detection in complex networks was made using the genetic algorithm, a new method of community detection in complex networks was presented using the genetic algorithm structure. The single-iteration label propagation method was utilized to make population initializing, a strategy of unified label crossover against crossing difficulty of string representation was proposed, and the directional mutation strategy was adopted to solve the defects of random mutation in the ge-netic algorithm. The experimental results show that for the typical computer-generated network structure and real-world network structure, the method can detect community structure more accurately. Compared with the classical algorithms, it has a higher precision of community detection and a faster convergence speed.