Quantum-behaved discrete multi-objective particle swarm optimization for complex network clustering
Quantum-behaved discrete multi-objective particle swarm optimization for complex network clustering
复制标题
复杂网络聚类的量子行为离散多目标粒子群优化
DOI：
10.1016/j.patcog.2016.09.013
复制
发表时间：
2017-03-01
影响因子：
8
通讯作者：
Gong, Maoguo
中科院分区：
文献类型：
--
作者：
Li, Lingling;Jiao, Licheng;Gong, Maoguo
复杂网络的研究已经引起了学术界和各个应用领域的广泛关注。复杂网络聚类是复杂网络研究的核心问题之一，它研究的是复杂网络中节点的内部组织。离散粒子群优化策略已被成功提出用于网络聚类，但现有方法的鲁棒性较弱。本文将复杂网络聚类任务建模为多目标优化问题，并使用基于量子机制的粒子群优化算法（一种并行算法）来解决该问题。据我们所知，这是首次尝试将基于量子机制的离散粒子群优化算法应用于网络聚类。此外，采用非显性排序选择操作进行个体替换。在此基础上，提出了一种基于量子行为的离散多目标粒子群优化算法。实验结果表明，该算法在扩展Girvan和纽曼基准测试和真实网络上，尤其是在大规模网络上，具有较好的性能，与现有算法相比具有较强的竞争力. (C)2016爱思唯尔有限公司版权所有。
Complex network research has attracted lots of attention in both academic community and various application fields. Complex network clustering, as one of the key issues in complex network, explores the internal organization of the nodes in a complex network. The discrete particle swarm optimization strategy has been successfully proposed for network clustering, while the existing method works with weak robust. In this paper, we model the task of complex network clustering as a multi-objective optimization problem and solve the problem with the quantum mechanism based particle swarm optimization algorithm, which is a parallel algorithm. To our knowledge, this is the first attempt to apply the quantum mechanism based discrete particle swarm optimization algorithm into network clustering. In addition, the non-dominant sorting selection operation is employed for individual replacement. Consequently, a quantum-behaved discrete multi-objective particle swarm optimization algorithm is proposed for complex network clustering. The experimental results demonstrate that the proposed algorithm performs effectively and achieves competitive performance with the state-of-the-art approaches on the extension of Girvan and Newman benchmarks and real-world networks, especially on large-scale networks. (C) 2016 Elsevier Ltd. All rights reserved.