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一种改进的自适应步长的人工萤火虫算法

唐少虎1,2，刘小明1,2

(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)

摘要：在基本的人工萤火虫算法(GSO)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。

关键词：人工萤火虫算法；自适应步长；觅食行为；全局收敛性

DOI:10.3969/j.issn.1673-4785.201403025

中图分类号：TP183 文献标志码：A

收稿日期：2014-03-09. 网络出版日期：2015-04-07.

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：

中文引用格式：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(3): 470-475.

英文引用格式：TANG Shaohu, LIU Xiaoming. An improved adaptive step glowworm swarm optimization algorithm[J]. CAAI Transactions on Intelligent Systems, 2015, 10(3): 470-475.

An improved adaptive step glowworm swarm optimization algorithm

TANG Shaohu1,2, LIU Xiaoming1,2

(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)

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.

Keywords：glowworm swarm optimization; adaptive step; foraging behavior; global convergence

通信作者：唐少虎. E-mail: tshaohu@163.com.

大自然中的萤火虫种类多种多样，萤火虫通过身体发光来达到各种生存目的。……