基于自适应Memetic算法的多目标复杂网络社区检测

针对提高复杂网络社区检测准确度问题, 提出了一种自适应Memetic算法的多目标社区检测算法。在全局搜索中利用Logistic函数来设置与全局优化相应的交叉概率和变异概率，并将多目标优化问题转化成同时最小优化Kernel K-Means和Ratio Cut这两个目标函数；在局部搜索中利用权重将两个目标函数合并成一个局部优化目标，并采用爬山搜索来寻找个体最优。在虚拟和真实网络实验平台下，与五个基于遗传算法的方法以及Fast Modularity算法相比，结果表明算法能有效提高社区检测准确度，具有更好的寻优效果。

Multi-objective community detection in complex networks based on adaptive memetic algorithm

In order to improve the accuracy of the community detection in complex networks, this paper proposed a multi-objective community detection based on adaptive memetic algorithm. In global search, the algorithm applied the Logistic function to set the corresponding crossover probability and mutation probability, and turned the multi-objective optimization problem into minimal optimization of two objectives called Kernel K-Means(KKM) and Ratio Cut(RC) at the same time; In local search, the local optimization target was constituted of weights of two objective functions and a hill-climbing strategy was used to find the best individual. Experiments on synthetic and real life networks show that, compared with five algorithms based on GAs(Genetic Algorithms) and Fast Modularity algorithm, the proposed algorithm can effectively improve the accuracy of the community detection and has certain advantages in solving community detection problems in complex networks .