深度学习萤火虫算法

doi:10.3969/j.issn.0372-2112.2018.11.010

电子学报 ›› 2018, Vol. 46 ›› Issue (11): 2633-2641.DOI: 10.3969/j.issn.0372-2112.2018.11.010

深度学习萤火虫算法

赵嘉^1,2,3, 谢智峰¹, 吕莉^1,2,3, 王晖^1,2,3, 孙辉^1,2,3, 喻祥^1,3

1. 南昌工程学院信息工程学院, 江西南昌 330099;
2. 鄱阳湖流域水工程安全与资源高效利用国家地方联合工程实验室, 江西南昌 330099;
3. 江西省水信息协同感知与智能处理重点实验室, 江西南昌 330099

收稿日期:2017-11-10 修回日期:2018-01-25 出版日期:2018-11-25
- 作者简介:
- 赵嘉男,1981年9月生,安徽桐城人.教授、硕士生导师、中国电子学会高级会员.主要研究方向为智能计算与大数据挖掘.E-mail:zhaojia925@163.com;谢智峰 男,1993年9月生,江西南昌人.在读研究生.主要研究方向为智能计算;吕莉女,1982年5月生,江西贵溪人.副教授,硕士生导师.主要研究方向为智能计算和运动目标跟踪;王晖男,1982年8月生,湖北红安人.博士、副教授、硕士生导师.主要研究方向为智能计算和并行计算;孙辉男,1959年1月生,江西九江人.博士、教授、硕士生导师.主要研究方向为智能计算和Rough集;喻祥男,1983年7月生,江西景德镇人.博士、讲师.主要研究方向为智能计算与计算智能.
- 基金资助:
- 国家自然科学基金 (No.51669014，No.61663029，No.61663028，No.61703199）; 江西省杰出青年基金 (No.2018ACB21029）

Firefly Algorithm with Deep Learning

ZHAO Jia^1,2,3, XIE Zhi-feng¹, LÜ Li^1,2,3, WANG Hui^1,2,3, SUN Hui^1,2,3, YU Xiang^1,3

1.School of Information Engineering, Nanchang Institute of Technology, Nanchang, Jiangxi 330099, China;
2.National-Local Engineering Laboratory of Water Engineering Safety and Effective Utilization of Resources in Poyang Lake Area, Nanchang, Jiangxi 330099, China;
3.Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang, Jiangxi 330099, China

Received:2017-11-10 Revised:2018-01-25 Online:2018-11-25 Published:2018-11-25
- Supported by:
- National Natural Science Foundation of China (No.51669014, No.61663029, No.61663028, No.61703199); Outstanding Youth Fund of Jiangxi Province (No.2018ACB21029)

摘要/Abstract

摘要： 为克服萤火虫算法全局寻优精度不高和过早收敛的缺点，本文提出深度学习萤火虫算法.算法采用随机吸引模型，萤火虫随机选择一个粒子学习，根据历史最优位置构建广义中心粒子，对其进行一定次数的单维深度学习，学习后的粒子引导种群进化.实验发现，深度学习策略及粒子深度学习次数对算法优化性能的改善起着重要作用.12个基准测试函数的实验结果表明，算法的综合寻优性能优于其它8种最近提出的萤火虫算法.

关键词: 全局寻优, 随机吸引模型, 广义中心粒子, 深度学习, 萤火虫算法

Abstract: 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.

Key words: global optimization, random attraction model, general center particle, deep learning, firefly algorithm

中图分类号:

TP182
TP391

. 深度学习萤火虫算法[J]. 电子学报, 2018, 46(11): 2633-2641.

. Firefly Algorithm with Deep Learning[J]. Acta Electronica Sinica, 2018, 46(11): 2633-2641.

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深度学习萤火虫算法

Firefly Algorithm with Deep Learning

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