Hybrid genetic grey wolf algorithm for high dimensional complex function optimization

被引：0
|
作者：
Gu Q.-H. [1 ]
Li X.-X. [1 ]
Lu C.-W. [1 ]
Ruan S.-L. [1 ]
机构：
[1] School of Management, Xi'an University of Architecture and Technology, Xi'an
来源：
Kongzhi yu Juece/Control and Decision | 2020年 / 35卷 / 05期
关键词：
Genetic operator; Grey wolf optimizer; High dimensional function optimization algorithm; Opposition-based learning; Population partition;
D O I：
10.13195/j.kzyjc.2018.1253
中图分类号：
学科分类号：
摘要：
High-dimensional function optimization usually refers to the function optimization problem with dimension over 100, which is difficult to be solved for the existence of "dimension disaster". In this paper, three genetic operators are embedded into the basic grey wolf algorithm, and a hybrid genetic-grey wolf algorithm (HGGWA) is proposed. The global convergence of the HGGWA is greatly improved by combining the advantages of the GWO and GA. The current three optimal individuals are disturbed by the diversity mutation operator in the process of the search so as to avoid the possibility of falling into local optimum. The performance of the algorithm is verified using 13 standard benchmark functions and 10 high dimensional functions, and the optimization results are compared with the PSO, GSA, GWO and 9 improved algorithms. Simulation results show that the HGGWA is greatly improved in convergence accuracy, which verify the effectiveness of the HGGWA in solving high-dimensional functions. © 2020, Editorial Office of Control and Decision. All right reserved.
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页码：1191 / 1198
页数：7
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