Firefly Algorithm with Deep Learning

被引：0

作者：

Zhao J. ^{[1
,2
,3
]}

Xie Z.-F. ^{[1
]}

Lü L. ^{[1
,2
,3
]}

Wang H. ^{[1
,2
,3
]}

Sun H. ^{[1
,2
,3
]}

Yu X. ^{[1
,3
]}

机构：

[1] School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, Jiangxi

[2] National-Local Engineering Laboratory of Water Engineering Safety and Effective Utilization of Resources in Poyang Lake Area, Nanchang, 330099, Jiangxi

[3] Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang, 330099, Jiangxi

来源：

| 2018年 / Chinese Institute of Electronics卷 / 46期

关键词：

Deep learning; Firefly algorithm; General center particle; Global optimization; Random attraction model;

D O I：

10.3969/j.issn.0372-2112.2018.11.010

中图分类号：

学科分类号：

摘要：

In order to overcome low precision and premature convergence of firefly algorithm, this paper proposes a new method, called firefly algorithm with deep learning. First, firefly algorithm selects a particle to learn according to the random attraction model; second, the method constructs a general center particle based on the best historical position; third, the particle leads the evolution of the population after a certain times of one-dimensional deep learning. Experiments show that the deep learning strategy and the number of deep learning of particles play an important role in optimizing the performance of the algorithm. The experimental results of 12 benchmark functions demonstrate that the comprehensive optimization performance of the proposed algorithm outperforms eight other recently firefly algorithm variants. © 2018, Chinese Institute of Electronics. All right reserved.

引用

页码：2633 / 2641

页数：8

共 27 条

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