改进吸引度的动态搜索萤火虫算法<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201706007

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Abstract Due to the decreasing attraction in solving high-dimensional optimization problems, the basic firefly algorithm easily falls into local optimum with low accuracy. Aiming at this problem, the dynamic search firefly algorithm based on improved attraction(ADFA) is proposed in this paper. The minimum attraction concept is presented to enhance the individual communication. By adding the optimal value of objective function, the step size can be adjusted adaptively. Finally, ten benchmark functions from CEC2014 are exploited to validate ADFA. The simulation results show that ADFA obtains higher convergence rate and better accuracy.

Key words： Firefly Algorithm(FA) Function Optimization Minimum Attraction Dynamic Search

Received: 23 December 2016

ZTFLH:

TP 183

Fund:Supported by Natural Science Foundation of the Higher Education Institutions of Jiangsu Province(No.12KJB510007)

About author:: (LI Rongyu, born in 1977, Ph.D., profe-ssor. His research interests include machine learning and artificial intelligence.)
(CHEN Qingqian(Corresponding author), born in 1991, master student. Her research interests include artificial intelligence.)
(CHEN Siyuan, born in 1996, undergra-duate student. His research interests include artificial intelligence.)

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	LI Rongyu
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Cite this article:

LI Rongyu,CHEN Qingqian,CHEN Siyuan. Dynamic Search Firefly Algorithm Based on Improved Attraction[J]. , 2017, 30(6): 538-548.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201706007 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2017/V30/I6/538

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