[1]唐少虎,刘小明.一种改进的自适应步长的人工萤火虫算法[J].智能系统学报,2015,10(03):470-475.[doi:10.3969/j.issn.1673-4785.201403025]
 TANG Shaohu,LIU Xiaoming.An improved adaptive step glowworm swarm optimization algorithm[J].CAAI Transactions on Intelligent Systems,2015,10(03):470-475.[doi:10.3969/j.issn.1673-4785.201403025]
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一种改进的自适应步长的人工萤火虫算法(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第10卷
期数:
2015年03期
页码:
470-475
栏目:
出版日期:
2015-06-25

文章信息/Info

Title:
An improved adaptive step glowworm swarm optimization algorithm
作者:
唐少虎12 刘小明12
1. 北方工业大学 电气与控制工程学院, 北京 100144;
2.北方工业大学 城市道路交通智能控制技术北京市重点实验室, 北京 100144
Author(s):
TANG Shaohu12 LIU Xiaoming12
1. College of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China;
2. Beijing Key Lab of Urban Intelligent Traffic Control Technology, North China University of Technology, Beijing 100144, China
关键词:
人工萤火虫算法自适应步长觅食行为全局收敛性
Keywords:
glowworm swarm optimizationadaptive stepforaging behaviorglobal convergence
分类号:
TP183
DOI:
10.3969/j.issn.1673-4785.201403025
文献标志码:
A
摘要:
在基本的人工萤火虫算法中, 萤火虫的固定移动步长导致算法容易陷入局部最优并可能出现函数适应值的震荡现象.在一些自适应步长的人工萤火虫算法(A-GSO)中, 算法迭代过程中会出现一些萤火虫的邻域集合为空集的现象, 这将导致算法收敛速度降低并陷入局部最优值.为此, 设计了改进的自适应步长的人工萤火虫算法(FA-GSO), 改进的算法针对邻域无同伴的萤火虫引入觅食行为寻找优化方向并自适应调整移动步长, 进一步提高求解精度和稳定性, 并给出了算法的收敛性分析, 结合GSO、A-GSO 2种算法对多个标准测试函数进行寻优并提取相关指标.通过指标对照, 验证了FA-GSO算法的有效性, 表明算法可以改善函数寻优的精度并提高迭代速度.
Abstract:
In the basic glowworm swarm optimization (GSO), it is easy to fall into local optimum and the oscillation phenomenon of function adaptive values may occur because of the fixed step length. In some adaptive-step glowworm swarm optimization (A-GSO) algorithms, neighborhood sets of some fireflies may be empty in the iterative process of the algorithm, which leads to lower convergence speed and falls into local optimal value. Therefore, an improved foraging-behavior adaptive-step GSO (FA-GSO) algorithm was designed. The foraging behavior of the fireflies without neighborhood peer and adaptive step is introduced in order to find the optimization direction in the improved algorithm. The precision, stability, and global convergence analysis of FA-GSO is presented. After extracting and comparing the relevant optimization indicators of GSO, A-GSO and FA-GSO by several standard test functions, the effectiveness of the FA-GSO algorithm was verified, which indicates that the improved algorithm can improve the accuracy of function optimization and the iteration speed.

参考文献/References:

[1] LIAO Wenhua, KAO Yucheng, LI Yingshan. A sensor deployment approach using glowworm swarm optimization algorithm in wireless sensor networks[J]. Expert Systems with Applications, 38(10): 12180-12188.
[2] KRISHNANAND K N D, GHOSE D. Glowworm swarm based optimization algorithm for multimodal functions with collective robotics applications[J]. Multiagent and Grid Systems, 2006, 2(3) 209-222.
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备注/Memo

备注/Memo:
收稿日期:2015-3-9;改回日期:。
基金项目:国家自然科学基金资助项目(61374191);国家“863”计划资助项目(2012AA112401);“十二五”国家科技支撑计划课题专项经费资助项目(2014BAG03B01).
作者简介:唐少虎,男,1986年生,博士研究生,主要研究方向为交通控制、群智能算法.刘小明,男,1974年生,教授,博士,主要研究方向为交通控制、交通流理论.近年来主持国家级、省部级科研项目8项,获大连市科学技术一等奖1项,北京市科学技术成果二、三等奖各1项,中国智能交通协会科学技术奖二等奖1项.申请发明专利3项,授权发明专利1项,授权实用新型专利1项,获软件著作权4项.发表学术论文40余篇,出版专著1部、译著1部.
通讯作者:唐少虎. E-mail: tshaohu@163.com.
更新日期/Last Update: 2015-07-15