[1]赵传武,黄宝柱,阎跃观,等.一种非线性权重的自适应鲸鱼优化算法[J].计算机技术与发展,2020,30(10):7-13.[doi:10. 3969 / j. issn. 1673-629X. 2020. 10. 002]
　ZHAO Chuan-wu,HUANG Bao-zhu,YAN Yue-guan,et al.An Adaptive Whale Optimization Algorithm of Nonlinear Inertia Weight[J].,2020,30(10):7-13.[doi:10. 3969 / j. issn. 1673-629X. 2020. 10. 002]
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一种非线性权重的自适应鲸鱼优化算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
30
期数:
2020年10期
页码:
7-13
栏目:
智能、算法、系统工程
出版日期:
2020-10-10

文章信息/Info

Title:
An Adaptive Whale Optimization Algorithm of Nonlinear Inertia Weight
文章编号:
1673-629X(2020)10-0007-07
作者:
赵传武1; 黄宝柱2; 阎跃观1; 代文晨1; 张 建1
1. 中国矿业大学(北京) 地球科学与测绘工程学院,北京 100083; 2. 开滦(集团)有限责任公司,河北 唐山 063018
Author(s):
ZHAO Chuan-wu1; HUANG Bao-zhu2; YAN Yue-guan1; DAI Wen-chen1; ZHANG Jian1
1. School of Geoscience and Surveying Engineering,China University of Mining and Technology, Beijing 100083,China; 2. Kailuan Group Co. ,Ltd. ,Tangshan 063018,China
关键词:
非线性权重; 位置更新; 加权策略; 鲸鱼优化算法; 结构简单
Keywords:
nonlinear weight; position update; weighting strategy; whale optimization algorithm; simple structure
分类号:
TP301. 6
DOI:
10. 3969 / j. issn. 1673-629X. 2020. 10. 002
摘要:
随着现实生活中待优化问题的复杂度增加,种群优化算法得到迅速发展。 目前,各种鲸鱼优化算法被提出,但是在不断提高精度的同时,却增加了算法的复杂性。 针对鲸鱼优化算法(WOA)收敛速度慢、求解精度低的问题,在优化算法性能的基础上保留鲸鱼优化算法结构简单的特点,提出了基于非线性权重的自适应鲸鱼优化算法(NWAWOA)。 通过非线性权重 S1 和 S2 对鲸鱼优化算法三个阶段的位置更新公式采用两种不同的加权策略,在平衡算法全局搜索与局部开发能力的同时,加快收敛速度、提高求解精度。 在 10 个经典测试函数上的实验表明,改进的算法与经典粒子群算法(PSO)、WOA、WOAWC 算法、EWOA 算法相比具有较好的收敛速度、求解精度和稳定性,同时算法结构简单,易于学习。
Abstract:
With the increasing complexity of the problems to be optimized in real life,the population optimization algorithm has developed rapidly. At present, various whale optimization algorithms have been proposed,but while the accuracy has been continuously improved,the algorithm complexity has been increased. Aiming at the problem of the slow convergence speed and low solution precision in the whale optimization algorithm (WOA), retaining the simple structure of WOA on the basis of optimizing algorithm performance, an adaptive WOA based on nonlinear weight (NWAWOA) is proposed. Two different weighting strategies are applied to the three-stage position update formula of the whale optimization algorithm by nonlinear weights S1 and S2. While balancing the global search and local exploitation capabilities of the algorithm,the convergence speed is accelerated and the solution precision is improved. Experiments on 10 classic test functions show that the improved algorithm has better convergence speed, solution precision and stability than the classic particle swarm optimization (PSO),WOA,WOAWC and EWOA. At the same time, with simple structure and easy learning.
更新日期/Last Update: 2020-10-10