|Table of Contents|

Elite Opposition Learning-Based Dimension by DimensionImproved Dragonfly Algorithm(PDF)

《南京师大学报(自然科学版)》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2019年03期
Page:
65-72
Research Field:
·全国机器学习会议论文专栏·
Publishing date:

Info

Title:
Elite Opposition Learning-Based Dimension by DimensionImproved Dragonfly Algorithm
Author(s):
He Qing12Huang Minming12Wang Xu12
(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)
Keywords:
dragonfly algorithmelite opposition-based learningfunction optimizationinterdimensional interference
PACS:
TP301
DOI:
10.3969/j.issn.1001-4616.2019.03.009
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.

References:

[1] MIRJALILI S. Dragonfly algorithm:a new meta-heuristic optimization technique for solving single-objective,discrete,and multi-objective problems[J]. Neural computing & applications,2016,27(4):1053-1073.
[2]ABDEL B M,LUO Q F,MIAO H,et al. Solving 0-1 knapsack problems by binary dragonfly algorithm[C]//International Conference on Intelligent Computing. Liverpool:Springer,2017.
[3]THARWAT A,GABEL T,HASSANIEN A E. Parameter optimization of support vector machine using dragonfly algorithm[C]//Proceedings of the International Conference on Advanced Intelligent Systems and Informatics. Cairo:Springer,2017.
[4]赵齐辉,杜兆宏,刘升,等. 差分进化的蜻蜓算法[J]. 微电子学与计算,2018,35(7):101-105.
[5]吴伟民,吴汪洋,林志毅,等. 基于增强个体信息交流的蜻蜓算法[J]. 计算机工程与应用,2017,53(4):10-15.
[6]SREE R K S,MURUGAN S. Memory based hybrid dragonfly algorithm for numerical optimization problems[J]. Expert systems with applications,2017,83(1):63-78.
[7]韩鹏,陈锋. 一种改进的多目标蜻蜓优化算法[J]. 信息技术与网络安全,2017,36(30):27-31.
[8]VISWANATHAN G M,AFANASYEV V,BULDYREV S V,et al. Levy flight search patterns of wandering albatrosses[J]. Nature,1996,361(6581):413-415.
[9]TIZHOOSH H R. Opposition-based learning:a new scheme for machine intelligence[C]//Computational Intelligence for Modelling. Vienna:Computer society,2005.
[10]WEI W H,ZHOU J L,FANG C,et al. Constrained differential evolution using generalized opposition-based learning[J]. Acta electronica sinica,2016,20(11):4413-4437.
[11]ZHANG S,LUO Q F,ZHOU Y Q. Hybrid grey wolf optimizer using elite opposition-based learning strategy and simplex method[J]. International journal of computational intelligence and applications,2017,16(2):1-12.
[12]AHANDANI M A,ALAVI R H. Opposition-based learning in shuffled frog leaping:an application for parameter identify cation[J]. Information sciences,2015,291(291):19-42.
[13]赵挺,孟子航,沈海斌. 基于反向学习与Levy飞行的改进蜂群算法[J]. 传感器与微系统,2017,36(1):111-115.
[14]王李进,尹义龙,钟一文. 逐维改进的布谷鸟搜索算法[J]. 软件学报,2013,24(11):2687-2698.
[15]LIANG J J,QIN A K,SUGANTHAN P N,et al. Comprehensive learning particle swarm optimizer for global optimization of multimodal functions[J]. IEEE transaction on evolutionary computation,2006,10(3):281-296.
[16]马骏,项铁铭. 一种基于佳点集原理与引力搜索的新型蜻蜓算法[J]. 软件导论,2018,12(1):85-89.
[17]范帅军.布谷鸟搜索算法的应用研究与改进[D]. 成都:西南交通大学. 2016.
[18]CAI Z F,YANG X D. Cuckoo search algorithm with deep search[C]//Proceedings of the 3rd IEEE International Conference on Computer and Communications(ICCC). Chengdu:IEEE Publications,2018.
[19]MIRJALILI S,HASHIMM S Z M. A new hybrid PSOGSA algorithm for function optimization[C]//2010 International Conference on Computer and Information Application(ICCIA). Tianjin:IEEE Publications,2010.

Memo

Memo:
-
Last Update: 2019-09-30