[1]何 庆,黄闽茗,王 旭.基于精英反向学习的逐维改进蜻蜓算法[J].南京师范大学学报(自然科学版),2019,42(03):65-72.[doi:10.3969/j.issn.1001-4616.2019.03.009]
　He Qing,Huang Minming,Wang Xu.Elite Opposition Learning-Based Dimension by DimensionImproved Dragonfly Algorithm[J].Journal of Nanjing Normal University(Natural Science Edition),2019,42(03):65-72.[doi:10.3969/j.issn.1001-4616.2019.03.009]
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基于精英反向学习的逐维改进蜻蜓算法()
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《南京师范大学学报》（自然科学版）[ISSN:1001-4616/CN:32-1239/N]

卷:
第42卷
期数:
2019年03期
页码:
65-72
栏目:
·全国机器学习会议论文专栏·
出版日期:
2019-09-30

文章信息/Info

Title:
Elite Opposition Learning-Based Dimension by DimensionImproved Dragonfly Algorithm
文章编号:
1001-4616(2019)03-0065-08
作者:
何 庆1; 2; 黄闽茗1; 2; 王 旭1; 2
(1.贵州大学大数据与信息工程学院,贵州 贵阳 550025)(2.贵州大学贵州省公共大数据重点实验室,贵州 贵阳 550025)
Author(s):
He Qing1; 2; Huang Minming1; 2; Wang Xu1; 2
(1.College of Big Data and Information Engineering,Guizhou University,Guiyang 550025,China)(2.Guizhou Provincial Key Laboratory of Public Big Data,Guizhou University,Guiyang 550025,China)
关键词:
蜻蜓算法; 精英反向学习; 函数优化; 维间干扰
Keywords:
dragonfly algorithm; elite opposition-based learning; function optimization; interdimensional interference
分类号:
TP301
DOI:
10.3969/j.issn.1001-4616.2019.03.009
文献标志码:
A
摘要:
针对蜻蜓算法(DA)寻优精度不高、收敛速度慢及后期搜索活力不足等问题,提出了基于精英反向学习的逐维改进蜻蜓算法(EDDA). 首先,利用精英反向学习策略初始化种群,以增强种群多样性,提高搜索效率; 其次,利用逐维更新策略对蜻蜓个体进行更新,减少维间干扰,有效提高了算法的寻优能力; 最后,充分利用当前解的信息双向搜索,提升了解的搜索活力. 通过9个测试函数的实验结果表明,该算法相比较于标准蜻蜓算法,寻优精度更高、收敛速度更快及后期搜索活力更强,与其他改进算法相比也具有一定的竞争优势.
Abstract:
Dragonfly algorithm(DA)is a widely applied algorithm,however,it still has some shortcomings such as low convergence precision,slow convergence speed and weak search vitality. Elite opposition learning-based dimension by dimension improved dragonfly algorithm(EDDA)was proposed. Firstly,the elite opposition-based learning strategy was used to initialize the population,its result enhanced population diversity and improved the search efficiency. Secondly,the individual of dragonfly was updated with the strategy of dimension-by-dimension to reduce interdimensional interference and effectively improved the search ability of the algorithm. Finally,the information of the current solution was fully utilized to bilateral search to enhance the search vitality of the algorithm. The experimental results of nine test functions show that compared with the standard dragonfly algorithm,the proposed algorithm has higher precision,faster convergence speed and stronger search vitality. Compared with other improved algorithms,it has certain competitive advantage.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2019-07-05.基金项目:贵州省科技计划项目重大专项(黔科合重大专项字[2018]3002)、贵州省公共大数据重点实验室开放课题(2017BDKFJJ004、2017BDKFJJ034)、贵州省教育厅青年科技人才成长项目(黔科合KY字[2016]124)、贵州省科技计划项目重大专项(黔科合重大专项字[2016]3022). 通讯联系人:王旭,博士,副教授,研究方向:机器学习应用、进化计算、量子通讯. E-mail:xuwang@gzu.edu.cn
更新日期/Last Update: 2019-09-30