您的位置：山东大学 -> 科技期刊社 -> 《山东大学学报（工学版）》

山东大学学报 (工学版) ›› 2023, Vol. 53 ›› Issue (4): 93-103.doi: 10.6040/j.issn.1672-3961.0.2022.128

• 机器学习与数据挖掘 • 上一篇    

混合改进策略的阿奎拉鹰优化算法

刘庆鑫1,齐琦1*,贾鹤鸣2,李霓3,4   

  1. 1.海南大学计算机科学与技术学院, 海南 海口 570228;2.三明学院信息工程学院, 福建 三明 365004;3.海南师范大学数学与统计学院, 海南 海口 571158;4.海南师范大学数据科学与智慧教育教育部重点实验室, 海南 海口 571158
  • 发布日期:2023-08-18
  • 作者简介:刘庆鑫(1997— ),男,福建福州人,硕士研究生,主要研究方向为群体智能与图像分割. E-mail:qxliu@hainanu.edu.cn. *通信作者简介:齐琦(1978— ),男,北京人,副研究员,博士,主要研究方向为组合优化算法与机器学习. E-mail:qqi@hainanu.edu.cn
  • 基金资助:
    国家自然科学基金资助项目(11861030);海南省自然科学基金资助项目(2019RC176,621RC511);福建省自然科学基金资助项目(2021J011128);海南省研究生创新科研课题资助项目(Qhys2021-190)

Aquila optimizer based on hybrid improved strategies

LIU Qingxin1, QI Qi1*, JIA Heming2, LI Ni3,4   

  1. 1. School of Computer Science and Technology, Hainan University, Haikou 570228, Hainan, China;
    2. School of Information Engineering, Sanming University, Sanming 365004, Fujian, China;
    3. School of Mathematics and Statistics, Hainan Normal University, Haikou 571158, Hainan, China;
    4. Key Laboratory of Data Science and Smart Education, Ministry of Education, Hainan Normal University, Haikou 571158, Hainan, China
  • Published:2023-08-18

PDF (PC)

75

摘要： 针对阿奎拉鹰优化算法(Aquila optimizer, AO)收敛速度慢、易陷入局部最优且寻优精度较低等问题,提出混合改进策略的阿奎拉鹰优化算法(Aquila optimizer based on hybrid improved strategies, HH-SAO)。初始化阶段引入准反向学习策略,增强初始化种群多样性。引入正弦波随机策略,提高算法全局探索阶段随机性,提升算法全局寻优能力。利用哈里斯鹰算法(Harris hawks optimization, HHO)的4种攻击策略替换原AO算法的局部开发阶段策略,提高算法跳出局部极小值能力;引入能量缩减机制实现全局与局部阶段的动态转换,平衡算法全局探索和局部开发能力。仿真试验选取23个基准测试函数和1个经典工程设计问题进行性能测试,结果表明改进算法相较于其他流行算法具有更好的寻优能力和工程适用性。

关键词: 阿奎拉鹰优化算法, 哈里斯鹰优化算法, 准反向学习, 正弦波随机策略, 能量缩减机制

中图分类号: 

  • TP301.6
[1] 贾鹤鸣, 李瑶, 孙康健. 基于遗传乌燕鸥算法的同步优化特征选择[J]. 自动化学报, 2022, 48(6): 1601-1615. JIA Heming, LI Yao, SUN Kangjian. Simultaneous feature selection optimization based on hybrid sooty tern optimization algorithm and genetic algorithm[J]. Acta Automatica Sinica, 2022, 48(6): 1601-1615.
[2] 贾鹤鸣, 姜子超, 李瑶. 基于改进秃鹰搜索算法的同步优化特征选择[J]. 控制与决策, 2022, 37(2): 445-454. JIA Heming, JIANG Zichao, LI Yao. Simultaneous feature selection optimization based on improved bald eagle search algorithm[J]. Control and Decision, 2022, 37(2): 445-454.
[3] HOUSSEIN E H, HUSSAIN K, ABUALIGAH L, et al. An improved opposition-based marine predators algorithm for global optimization and multilevel thresholding image segmentation[J]. Knowledge-Based Systems, 2021, 229: 107348.
[4] 贾鹤鸣, 刘宇翔, 刘庆鑫, 等. 融合随机反向学习的黏菌与算术混合优化算法[J]. 计算机科学与探索, 2022, 16(5): 1182-1192. JIA Heming, LIU Yuxiang, LIU Qingxin, et al. Hybrid algorithm of slime mould algorithm and arithmetic optimization algorithm based on random opposition-based learning[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(5): 1182-1192.
[5] KENNEDY J, EBERHART R. Particle swarm optimization[C] //Proceedings of IEEE International Conference on Neural Networks. Perth, Australia: IEEE, 1995: 1942-1948.
[6] MIRJALILI S, LEWIS A. The whale optimization algorithm[J]. Advances in Engineering Software, 2016, 95: 51-67.
[7] MIRJALILI S, GANDOMI A H, MIRJALILI S Z, et al. Salp swarm algorithm: a bi-inspired optimizer for engineering design problems[J]. Advances in Engineering Software, 2017, 114: 163-191.
[8] MIRJALILI S. SCA: a sine cosine algorithm for solving optimization problems[J]. Knowledge-Based System, 2016, 96: 1-14.
[9] MIRJALILI S, MIRJALILI S M. Multi-verse optimizer: a nature-inspired algorithm for global optimization[J]. Neural Computing and Applications, 2015, 27(2): 495-513.
[10] MIRJALILI S, MIRJALILI S M, LEWIS A. Grey wolf optimizer[J]. Advances in Engineering Software, 2014, 69: 46-61.
[11] HEIDARI A A, MIRJALILI S, FARIS H, et al. Harris hawks optimization: algorithm and applications[J]. Future Generation Computer System, 2019, 97: 849-872.
[12] ABUALIGAH L, YOUSRI D, ELAZIZ M A, et al. Aquila optimizer: a novel meta-heuristic optimization algorithm[J]. Computer & Industrial Engineering, 2021, 157: 107250.
[13] WOLPERT D H, MACREADY W G. No free lunch theorems for optimization[J]. IEEE Transaction on Evolutionary Computing, 1997, 1: 67-82.
[14] WANG S, JIA H, LIU Q, et al. An improved hybrid aquila optimizer and Harris hawks optimization for global optimization[J]. Mathematical Biosciences and Engineering, 2021, 18(6): 7076-7109.
[15] MONIKA V, MINI S, MADHUSUDAN S. Application of metaheuristic technique to study influence of core material and core trench on performance of surface inset PMSM[J]. Arabian Journal for Science and Engineering, 2021, 4:1-17.
[16] ESPARZA E R, CALZADA L A Z, OLIVA D, et al. An efficient Harris hawks-inspired image segmentation method[J]. Expert Systems with Applications, 2020, 155: 113428.
[17] HAMOUR H, KAMEL S, MOHAMED E A. Harris hawks optimization technique for optimal reconfiguration of radial distribution networks[J]. Aswan University Journal of Science and Technology, 2021, 1(1): 1-16.
[18] 刘小龙, 梁彤缨. 基于方形邻域和随机数组的哈里斯鹰优化算法[J]. 控制与决策, 2022, 37(10): 2467-2476. LIU Xiaolong, LIANG Tongying. Harris hawk optimization algorithm based on square neighborhood and randomarray[J]. Control and Decision, 2022, 37(10): 2467-2476.
[19] 刘成汉, 何庆. 融合振荡禁忌搜索的自适应均衡优化算法[J]. 计算机工程与应用, 2022, 58(10): 68-75. LIU Chenghan, HE Qing. Adaptive equalization optimization algorithm combining oscillating tabu search[J]. Computer Engineering and Applications, 2022, 58(10): 68-75.
[20] TIZHOOSH H R. Opposition-based learning: a new scheme for machine intelligence[C] //Proceedings of International Conference on Computational Intelligence for Modelling, Control and Automation, and Intelligent Agent, Web Technologies and Internet Commerce. Vienna, Austria: IEEE, 2005: 695-701.
[21] BASE M. Quasi-oppositional differential evolution for optimal reactive power dispatch[J]. International Journal of Electrical Power and Energy Systems, 2015, 78: 29-40.
[22] RODRIGUEZ L, CASTILLO O, GARCIA M, et al. A new randomness approach based on sine wave to improve performance in metaheuristic algorithms[J]. Methodologies and Application, 2020, 24(9): 11989-12011.
[23] GUPTA S, DEEP K. A memory-based grey wolf optimizer for global optimization tasks[J]. Applied Soft Computing Journal, 2020, 93: 106367.
[1] 杨巨成,韩书杰,毛磊,代翔子,陈亚瑞. 胶囊网络模型综述[J]. 山东大学学报 (工学版), 2019, 49(6): 1-10.
[2] 方波,陈红梅. 一种新的双策略进化果蝇优化算法[J]. 山东大学学报 (工学版), 2019, 49(3): 22-31.
[3] 吴红岩,冀俊忠. 基于花授粉算法的蛋白质网络功能模块检测方法[J]. 山东大学学报(工学版), 2018, 48(1): 21-30.
[4] 周志杰,赵福均,胡昌华,王力,冯志超,刘涛源. 基于证据推理的航天继电器故障预测方法[J]. 山东大学学报(工学版), 2017, 47(5): 22-29.
[5] 任永峰,董学育. 基于自适应流形相似性的图像显著性区域提取算法[J]. 山东大学学报(工学版), 2017, 47(3): 56-62.
[6] 翟继友,周静波,任永峰,王志坚. 基于背景和前景交互传播的图像显著性检测[J]. 山东大学学报(工学版), 2017, 47(2): 80-85.
[7] 邬慧敏,吴璟莉. 重建二倍体个体单体型的改进环基算法[J]. 山东大学学报(工学版), 2016, 46(4): 9-14.
[8] 王立宏,李强. 旅行商问题的一种选择性集成求解方法[J]. 山东大学学报(工学版), 2016, 46(1): 42-48.
[9] 任永峰, 周静波. 基于信息弥散机制的图像显著性区域提取算法[J]. 山东大学学报(工学版), 2015, 45(6): 1-6.
[10] 文志强,朱文球,胡永祥. 半调图像的分类方法[J]. 山东大学学报(工学版), 2013, 43(4): 7-12.
[11] 徐姗姗,刘应安*,徐昇. 立体匹配中边界信息的强化算法[J]. 山东大学学报(工学版), 2012, 42(6): 43-49.
[12] 陈明志1,2, 陈健3, 许春耀3, 余轮3, 林柏钢1,2. 一种新的基于网络虚拟环境的用户访问模式聚类算法[J]. 山东大学学报(工学版), 2011, 41(6): 43-49.
[13] 吴天柱 . 基于RBF神经网络的彩色图像盲水印算法[J]. 山东大学学报(工学版), 2008, 38(2): 51-55 .
[14] 张劲松,李歧强,王朝霞 . 基于混沌搜索的混和粒子群优化算法[J]. 山东大学学报(工学版), 2007, 37(1): 47-50 .
[15] 贾银亮,张焕春,经亚枝,刘　晶 . 6步直线生成算法[J]. 山东大学学报(工学版), 2007, 37(1): 61-64 .
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 2018 《山东大学学报(工学版)》编辑部　
地址: 济南市山大南路27号（250100）　 电话: 0531-88366735　 E-mail: xbgxb@sdu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发