Faculty of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin 3419915195, Iran.
Department of Computer Engineering and Information Technology, Amirkabir University of Technology, Tehran 1591634311, Iran.
Chaos. 2020 Jan;30(1):013125. doi: 10.1063/1.5120094.
Community structure is one of the most important topological characteristics of complex networks. Detecting the community structure is a highly challenging problem in analyzing complex networks and it has high significance for understanding the function and organization of complex networks. A wide range of algorithms for this problem uses the maximization of a quality function called modularity. In this paper, a Chaotic Memetic Algorithm is proposed and used to solve the problem of the community structure detection in complex networks. In the proposed algorithm, the combination of the genetic algorithm (global search) and a dedicated local search is used to search the solution space. In addition, to improve the convergence speed and efficiency, in both global search and local search processes, instead of random numbers, chaotic numbers are used. By using chaotic numbers, the population diversity is preserved and it prevents from falling in the local optimum. The experiments on both real-world and synthetic benchmark networks indicate that the proposed algorithm is effective compared with state-of-the-art algorithms.
社区结构是复杂网络最重要的拓扑特征之一。检测社区结构是分析复杂网络的一个极具挑战性的问题，对于理解复杂网络的功能和组织具有重要意义。针对这个问题，已经提出了许多算法，这些算法都使用了称为模块度的一种质量函数的最大化。在本文中，提出并使用了一种混沌协同算法来解决复杂网络中的社区结构检测问题。在所提出的算法中，采用遗传算法（全局搜索）和专门的局部搜索相结合的方法来搜索解空间。此外，为了提高收敛速度和效率，在全局搜索和局部搜索过程中，使用混沌数代替随机数。通过使用混沌数，保持了种群的多样性，防止陷入局部最优。在真实网络和合成基准网络上的实验表明，与最先进的算法相比，所提出的算法是有效的。
