[1]宋美佳,贾鹤鸣,林志兴,等.融合非线性收敛因子与变异准反射学习的哈里斯鹰优化算法[J].智能系统学报,2024,19(3):738-748.[doi:10.11992/tis.202205008]
SONG Meijia,JIA Heming,LIN Zhixing,et al.Harris Hawks optimization algorithm based on nonlinear convergence factor and mutation quasi-reflected-based learning[J].CAAI Transactions on Intelligent Systems,2024,19(3):738-748.[doi:10.11992/tis.202205008]
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SONG Meijia,JIA Heming,LIN Zhixing,et al.Harris Hawks optimization algorithm based on nonlinear convergence factor and mutation quasi-reflected-based learning[J].CAAI Transactions on Intelligent Systems,2024,19(3):738-748.[doi:10.11992/tis.202205008]
融合非线性收敛因子与变异准反射学习的哈里斯鹰优化算法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
19
期数:
2024年第3期
页码:
738-748
栏目:
学术论文—人工智能基础
出版日期:
2024-05-05
- Title:
- Harris Hawks optimization algorithm based on nonlinear convergence factor and mutation quasi-reflected-based learning
- Keywords:
- Harris Hawks optimization; nonlinear convergence factor; quasi-reflected-based learning; quasi-inverse learning; chaotic mapping; engineering problems; meta-heuristic algorithms; swarm intelligence
- 分类号:
- TP301.6
- 文献标志码:
- 2023-10-18
- 摘要:
- 针对哈里斯鹰优化算法(Harris Hawks optimization, HHO)易早熟收敛、寻优精度低、收敛速度慢等问题，提出一种融合非线性收敛因子与变异准反射学习的哈里斯鹰优化算法（improved Harris Hawks optimization, IHHO）。首先，初始化阶段引入Circle混沌映射，提高初始化种群多样性和种群位置质量；其次，引入Sigmoid非线性收敛因子，平衡全局探索和局部开发能力；最后，针对HHO算法易陷入局部最优问题，提出变异准反射学习(quasi-reflection-based learning, QRBL)策略，提高种群活力，进一步提高算法局部收敛能力。仿真实验采用13个标准测试函数和1个经典工程问题对改进算法进行测试，结果表明改进算法收敛精度、收敛速度均有较大提高，适用于解决实际问题。
- Abstract:
- Due to the shortcomings of the Harris Hawks optimization (HHO) algorithm, such as premature convergence, low optimization precision, and slow convergence speed, an improved HHO (IHHO) algorithm integrating nonlinear convergence factor and mutation quasi-reflection-based learning (QRBL) is proposed. First, circle chaotic mapping is introduced in the initialization stage to improve the diversity of the initialization population and the location and quality of the population. Second, the sigmoid nonlinear convergence factor is introduced to balance the ability of global exploration and partial exploitation. Finally, because the HHO algorithm easily falls into the local optimum, mutation QRBL is proposed to improve the vigor of the population and further improve the local convergence ability of the algorithm. The simulation experiments are conducted by applying 13 standard test functions and one classical engineering problem to the evaluation of the proposed algorithm. The results show that the convergence accuracy and the convergence speed of the IHHO algorithm are greatly improved, and IHHO is suitable for solving practical problems.
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- 相似文献/References:
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[1]贾鹤鸣,刘庆鑫,刘宇翔,等.融合动态反向学习的阿奎拉鹰与哈里斯鹰混合优化算法[J].智能系统学报,2023,18(1):104.[doi:10.11992/tis.202108031]
JIA Heming,LIU Qingxin,LIU Yuxiang,et al.Hybrid Aquila and Harris hawks optimization algorithm with dynamic opposition-based learning[J].CAAI Transactions on Intelligent Systems,2023,18():104.[doi:10.11992/tis.202108031]
备注/Memo
收稿日期:2022-05-12。
基金项目:福建省教育厅中青年教师教育科研项目(JAT211002, JAT210432, JAT220834)；福建省高校教育技术研究会2022年度课题 (FJET202211, FJET202206)；福建省电子商务工程中心开放课题(KBX2109)；国家教育部教师函〔2021〕13号第二批人工智能助推教师队伍建设试点项目(三明学院)；福建省自然科学基金面上项目(2021J011128).
作者简介:宋美佳，助理实验师，主要研究方向为群体智能优化算法、数据挖掘。E-mail：20200125@fjsmu.edu.cn;贾鹤鸣，教授，主要研究方向为群体智能优化算法与工程应用。主持福建省自然科学基金等项目10余项。发表学术论文60余篇。E-mail：jiaheminglucky99@126.com;林志兴，高级实验师，主要研究方向为信息安全。E-mail：6051751@qq.com
通讯作者:贾鹤鸣. E-mail：jiaheminglucky99@126.com
更新日期/Last Update:
1900-01-01