引用本文:张新明,王霞,康强.改进的灰狼优化算法及其高维函数和FCM优化[J].控制与决策,2019,34(10):2073-2084
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改进的灰狼优化算法及其高维函数和FCM优化
张新明1,2, 王霞1, 康强1
(1. 河南师范大学计算机与信息工程学院,河南新乡453007;2. 河南省高校计算智能与数据挖掘工程技术研究中心,河南新乡453007)
摘要:
灰狼优化算法(GWO)具有较强的局部搜索能力和较快的收敛速度,但在解决高维和复杂的优化问题时存在全局搜索能力不足的问题.对此,提出一种改进的GWO,即新型反向学习和差分变异的GWO(ODGWO).首先,提出一种最优最差反向学习策略和一种动态随机差分变异算子,并将它们融入GWO中,以便增强全局搜索能力;然后,为了很好地平衡探索与开采能力以提升整体的优化性能,对算法前、后半搜索阶段分别采用单维操作和全维操作形成ODGWO;最后,将ODGWO用于高维函数和模糊C均值(FCM)聚类优化.实验结果表明,在许多高维Benchmark函数(30维、50维和1000维)优化上,ODGWO的搜索能力大幅度领先于GWO,与state-of-the-art优化算法相比,ODGWO具有更好的优化性能.在7个标准数据集的FCM聚类优化上, 与GWO、GWOepd和LGWO相比,ODGWO表现出了更好的聚类优化性能,可应用在更多的实际优化问题上.
关键词:  智能优化算法  灰狼优化算法  反向学习  差分变异  模糊C均值(FCM)聚类  高维函数优化
DOI:10.13195/j.kzyjc.2018.0146
分类号:TP181
基金项目:河南省高等学校重点科研项目(19A520026).
Improved grey wolf optimizer and its application to high-dimensional function and FCM optimization
ZHANG Xin-ming1,2,WANG Xia1,KANG Qiang1
(1.College of Computer and Information Engineering,Henan Normal University,Xinxiang453007,China;2.Engineering Technology Research Center for Computing Intelligence & Data Mining of Henan Province,Xinxiang453007,China)
Abstract:
The grey wolf optimizer (GWO) algorithm proposed recently has strong local search ability and fast convergence, but it has some defects, such as poor global search ability in solving high-dimensional functions and complex optimization problems.Therefore, this paper proposes an improved GWO, namely GWO with opposition- learning and differential mutation (ODGWO). Firstly, a max-min opposition learning strategy and a dynamical and random differential mutation operator are proposed, which are integrated with GWO to enhance its global search ability. Then, in order to balance exploration and exploitation well to improve the overall performance, one-dimensional operation and full-dimensional operation are applied to the first half and the latter one of the search phase respectively to form the ODGWO consequently. Finally, the ODGWO is used for the high-dimensional function and fuzzy C-means (FCM) clustering optimization.The experimental results on many high-dimensional (30, 50 and 1000 dimension) benchmark functions show that the ODGWO has significantly higher global search ability than the GWO does, and the ODGWO outperforms the state-of-the-art algorithms.As for FCM optimization on seven standard datasets, the ODGWO shows better clustering optimization performance compared with the GWO, the GWOepd and the LGWO, and it will be applied to more real-world optimization problems.
Key words:  intelligent optimization algorithm  grey wolf optimization  opposition-learning  differential mutation  fuzzy C-means (FCM) clustering  high-dimensional function optimization

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