[1]夏 超,欧阳平,李 明,等.基于混沌精英和 Levy 飞行策略的鲸鱼优化算法[J].计算机技术与发展,2024,34(04):180-186.[doi:10. 3969 / j. issn. 1673-629X. 2024. 04. 027]
　XIA Chao,OUYANG Ping,LI Ming,et al.Whale Optimization Algorithm Based on Chaotic Elite and Levy Flight Strategy[J].,2024,34(04):180-186.[doi:10. 3969 / j. issn. 1673-629X. 2024. 04. 027]
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基于混沌精英和 Levy 飞行策略的鲸鱼优化算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
34
期数:
2024年04期
页码:
180-186
栏目:
人工智能
出版日期:
2024-04-10

文章信息/Info

Title:
Whale Optimization Algorithm Based on Chaotic Elite and Levy Flight Strategy
文章编号:
1673-629X(2024)04-0180-07
作者:
夏 超1 ; 欧阳平1 ; 李 明2; 3 ; 屈盈飞2; 3* ; 郭玮峰1
1. 重庆工商大学 废油资源化技术与装备教育部工程研究中心,重庆 400067;
2. 重庆工商大学 检测控制集成系统工程实验室,重庆 400067;
3. 重庆工商大学 人工智能学院,重庆 400067
Author(s):
XIA Chao1 ; OUYANG Ping1 ; LI Ming2; 3 ; QU Ying-fei2; 3* ; GUO Wei-feng1
1. Engineering Research Center for Waste Oil Recovery Technology and Equipment of Ministry of Education, Chongqing Technology and Business University,Chongqing 400067,China;
2. Chongqing Engineering Laboratory for Detection,Control and Integrated System,Chongqing Technology and Business University,Chongqing 400067,China;
3. School of Artificial Intelligence,Chongqing Technology and Business University,Chongqing 400067,China
关键词:
鲸鱼优化算法; Tent 混沌映射; 反向学习策略; 非线性收敛因子; Levy 飞行策略
Keywords:
whale optimization algorithm;  Tent chaotic mapping;  opposition - based learning;  nonlinear convergence factor;  Levy flight strategy
分类号:
TP18
DOI:
10. 3969 / j. issn. 1673-629X. 2024. 04. 027
摘要:
针对鲸鱼优化算法(Whale Optimization Algorithm,WOA) 存在的收敛速度慢、精度低的问题,提出了基于 Tent 混沌精英和 Levy 飞行策略的鲸鱼优化算法( TELWOA) 。 使用 Tent 混沌映射初始化鲸鱼种群,保持种群的多样性,并通过引入精英反向学习策略,对初始种群的精英个体生成反向解,选取适应度高的种群作为下一代鲸鱼种群,加快算法收敛速度。其次,通过使用非线性收敛因子,缓解算法全局搜索和局部搜索能力不平衡的现象。 最后,在鲸鱼位置寻优过程中使用Levy 飞行策略,避免算法陷入局部最优,提升算法的全局搜索能力。 通过对不同改进策略的有效性分析、与其他智能算法的对比分析,证明了 TELWOA 算法在收敛精度、算法稳定性和全局寻优能力上与对比算法有显著提升,具有一定的实际工程应用能力。
Abstract:
For the problems of slow convergence and low accuracy of Whale Optimization Algorithm ( WOA) ,the WOA based on Tentchaotic Elite and Levy flight strategy ( TELWOA) is proposed. The whale population is initialized by Tent chaotic mapping to maintainthe population diversity,and the algorithm convergence speed is accelerated by introducing an elite opposition-based learning strategy togenerate the inverse solution for the elite individuals of the initial population and select the population with high adaptation as the nextgeneration whale population. Secondly,by using a nonlinear convergence factor,the imbalance between the algorithm’s global search andlocal search ability is alleviated. Finally,the Levy flight strategy is used in the whale location search process to avoid the algorithm fromfalling into local optimum and to improve the global search ability of the algorithm. By analyzing the effectiveness of differentimprovement strategies and comparing with other intelligent algorithms, it is proved that TELWOA has significant improvement inconvergence accuracy, algorithmic stability and global optimization searching ability with comparison algorithms, and it has certainpractical engineering application ability.

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更新日期/Last Update: 2024-04-10