| 一种改进的灰狼优化算法 |
| |
| 引用本文： | 陈贞1,闫明晗2. 一种改进的灰狼优化算法[J]. 延边大学学报(自然科学版), 2022, 0(3): 250-254 |
| |
| 作者姓名： | 陈贞1 闫明晗2 |
| |
| 作者单位： | (1.莆田学院 机电与信息工程学院, 福建 莆田 351100; 2.长春大学 电子信息工程学院, 长春 130022) |
| |
| 摘 要： | 为了克服标准灰狼优化(GWO)算法寻优精度不高,难以在收敛速度和避免陷入局部最优之间取得平衡等问题,提出了一种改进的灰狼优化(IGWO)算法.该算法采用非线性收敛因子策略和自适应调整策略来提高寻优精度和加快收敛速度.选取10个基准函数对IGWO算法进行验证表明,IGWO算法的优化精度和收敛速度显著优于标准GWO算法和其他元启发式算法,因此本文提出的IGWO算法在求解最优参数方面具有良好的应用价值.
|
| 关 键 词： | 灰狼优化算法 线性收敛因子 自适应调整策略 元启发式算法 |
An improved grey wolf optimization algorithm |
| |
| CHEN Zhen1,YAN Minghan2. An improved grey wolf optimization algorithm[J]. Journal of Yanbian University (Natural Science), 2022, 0(3): 250-254 |
| |
| Authors: | CHEN Zhen1 YAN Minghan2 |
| |
| Affiliation: | (1.College of Mechatronics and Information Engineering, Putian University, Putian 351100, China; 2.College of Electronic Information Engineering, Changchun University, Changchun 130022, China) |
| |
| Abstract: | An improved grey wolf optimization(IGWO)algorithm is proposed to overcome the problems of low optimization accuracy of standard grey wolf optimization(GWO)algorithm, difficulty of balance between the convergence speed and local optimization.IGWO algorithm utilizes nonlinear convergence factor strategy and adaptive adjustment strategy to improve the optimization accuracy, accelerate the convergence speed.Thus, 10 benchmark functions are selected to verify the IGWO algorithm.The results show that the optimization accuracy and convergence speed of the IGWO algorithm are significantly better than the standard GWO algorithm and particle swarm optimization algorithm.Consequently, the proposed IGWO algorithm in this paper exhibit positive application value in solving the optimal parameters. |
| |
| Keywords: | grey wolf optimization algorithm nonlinear convergence factor adaptive adjustment strategy meta - heuristic algorithm |
|
| 点击此处可从《延边大学学报(自然科学版)》浏览原始摘要信息 |
|
点击此处可从《延边大学学报(自然科学版)》下载免费的PDF全文 |
