Hybrid-Strategy Improved Golden Jackal Optimization

被引：2

作者：

Zhu, Xinglin ^{[1
]}

Wang, Tinghua ^{[1
]}

Lai, Zhiyong ^{[1
]}

机构：

[1] School of Mathematics and Computer Science, Gannan Normal University, Jiangxi, Ganzhou,341000, China

来源：

Computer Engineering and Applications | 2024年 / 60卷 / 04期

关键词：

Benchmarking - Support vector machines;

D O I：

10.3778/j.issn.1002-8331.2306-0099

中图分类号：

学科分类号：

摘要：

In view of the shortcomings of the golden jackal optimization (GJO) in solving complex optimization problems, such as slow convergence speed and being easy to fall into local optimum, a hybrid-strategy improved golden jackal optimization (IGJO) is proposed. Firstly, when the optimal solution of the algorithm stagnates updating, the Cauchy variation strategy is introduced to enhance the population diversity and improve the global search capability of the algorithm to avoid falling into local optimum. Then, a decision strategy based on weight is proposed to accelerate the convergence of the algorithm by assigning different weights to golden jackal individuals. Experiments with eight benchmark functions and some CEC2017 test functions show that the improved algorithm has better optimization performance and convergence speed. Furthermore, the improved algorithm is applied to optimize the parameters of support vector regression (SVR) model, and its effectiveness is verified by experiments on 5 UCI (University of California, Irvine) datasets. © The Author(s) 2024.

引用

页码：99 / 112