计算机工程与应用2024，Vol.60Issue(4)：99-112,14.DOI:10.3778/j.issn.1002-8331.2306-0099
混合策略改进的金豺优化算法
Hybrid-Strategy Improved Golden Jackal Optimization
摘要
Abstract
In view of the shortcomings of the golden jackal optimization(GJO)in solving complex optimization prob-lems,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 varia-tion 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 conver-gence 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.关键词
金豺优化算法/优化问题/柯西变异/权重Key words
golden jackal optimization/optimization problem/Cauchy variation/weight分类
信息技术与安全科学引用本文复制引用
朱兴淋,汪廷华,赖志勇..混合策略改进的金豺优化算法[J].计算机工程与应用,2024,60(4):99-112,14.基金项目
国家自然科学基金(61966002) （61966002）
江西省学位与研究生教育教学改革研究项目(JXYJG-2022-172). （JXYJG-2022-172）
