[1]陈杰,陈岁繁,李其朋.融合多策略的黄金正弦灰狼优化算法[J].浙江科技学院学报,2023,(06):514-526.[doi:10.3969/j.issn.1671-8798.2023.06.007 ]
 CHEN Jie,CHEN Suifan,LI Qipeng.Golden sine grey wolf optimization algorithm integrating multiple strategies[J].,2023,(06):514-526.[doi:10.3969/j.issn.1671-8798.2023.06.007 ]
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融合多策略的黄金正弦灰狼优化算法(/HTML)
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《浙江科技学院学报》[ISSN:1001-3733/CN:61-1062/R]

卷:
期数:
2023年06期
页码:
514-526
栏目:
出版日期:
2024-01-05

文章信息/Info

Title:
Golden sine grey wolf optimization algorithm integrating multiple strategies
文章编号:
1671-8798(2023)06-0514-13
作者:
陈杰陈岁繁李其朋
(浙江科技学院 机械与能源工程学院,杭州 310023)
Author(s):
CHEN Jie CHEN Suifan LI Qipeng
(School of Mechanical and Energy Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, Zhejiang, China)
关键词:
灰狼优化算法 收敛因子 比例权重 黄金正弦 代理模型
分类号:
TP301.6
DOI:
10.3969/j.issn.1671-8798.2023.06.007
文献标志码:
A
摘要:
【目的】为解决灰狼优化算法(grey wolf optimization,GWO)收敛精度不高,收敛速度较慢和易陷入局部最优等不足,提出一种融合多策略的黄金正弦灰狼优化算法(golden sine grey wolf optimization,G-GWO)。【方法】首先,利用非线性调整收敛因子、动态调整比例权重和引入黄金正弦策略对GWO算法进行改进; 然后,选取三类基准测试函数进行寻优实验,并与GWO算法、其他智能优化算法和其他改进GWO算法进行对比,从寻优的收敛精度、鲁棒性和收敛速度方面验证G-GWO算法的优越性; 最后,建立板料冲压成形工艺参数与质量参数的BP神经网络(BP neural network,BPNN)代理模型,选用8种算法分别优化BP神经网络的权值和阈值,对比优化后的代理模型精度,验证G-GWO算法在实际工程应用中的有效性。【结果】G-GWO算法在三类基准测试函数的收敛精度、鲁棒性和收敛速度较其他算法均有较大优势,优化后的代理模型最大减薄率相对误差为3.47%,最大增厚率相对误差为4.99%。【结论】改进策略能提高GWO算法的性能,这可作为建立高精度代理模型和后续的板料冲压工艺参数优化的参考。

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备注/Memo

备注/Memo:
收稿日期:2022-11-24
基金项目:国家重点研发计划(科技助力经济2020)项目(SQ2020YFF0423771); 浙江省科技计划项目(2020C01053,2022C04022)
通信作者:李其朋(1977— ),男,山东省临邑人,教授,博士,主要从事智能制造、数字孪生等研究。E-mail:liqipeng@zust.edu.cn。
更新日期/Last Update: 2023-12-31