[1]崔家瑞,李擎,杨柳祎,等.启发信息引导的改进萤火虫算法[J].哈尔滨理工大学学报,2019,(01):92-98.[doi:10.15938/j.jhust.2019.01.015]
 CUI Jia rui,LI Qing,YANG Liu yi,et al. Improved Firefly Algorithm Based on Heuristic Information[J].哈尔滨理工大学学报,2019,(01):92-98.[doi:10.15938/j.jhust.2019.01.015]
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启发信息引导的改进萤火虫算法
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《哈尔滨理工大学学报》[ISSN:1007-2683/CN:23-1404/N]

卷:
期数:
2019年01期
页码:
92-98
栏目:
电气与电子工程
出版日期:
2019-02-25

文章信息/Info

Title:
 Improved Firefly Algorithm Based on Heuristic Information

作者:
 崔家瑞李擎杨柳祎王恒张博钰
 (北京科技大学 自动化学院,北京 100083)
Author(s):
 CUI JiaruiLI QingYANG LiuyiWANG HengZHANG Boyu
(School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China)
关键词:
 关键词:萤火虫算法启发信息全局最优贝叶斯估计数值优化
Keywords:
 Keywords:firefly algorithm heuristic information global optimal Bayesian estimation numerical optimization
分类号:
TP18
DOI:
10.15938/j.jhust.2019.01.015
文献标志码:
A
摘要:
 摘要:萤火虫算法(FA)是一种群体智能优化算法,它基于萤火虫的闪烁和吸引特征模拟萤火虫的社会行为。为解决萤火虫算法后期收敛速度慢,易陷入局部最优的不足,对算法进行了改进。提出了两种启发信息引导算法收敛:第一种借鉴粒子群算法中“全局最优”的思想,将当前最优点的位置作为启发信息,形成了基于当前全局最优的萤火虫算法(FAGO);第二种将贝叶斯估计计算出的最优移动方向作为启发信息,形成了基于贝叶斯估计的萤火虫算法(FABE)。最后,将本文算法在多个常见函数上进行了测试,并与经典萤火虫算法、近年其他文献改进萤火虫算法进行了对比研究,结果表明本文所提算法能够加快收敛速度,提高收敛精度。
Abstract:
 Abstract:Firefly Algorithm (FA) is an optimization algorithm based on swarm intelligence which mimics the social behavior of fireflies based on the flashing and attraction characteristics of fireflies With the aim to address the disadvantages of the firefly algorithm of slow convergence speed and ease of falling into the local optimum in the later period of the evolution process, the firefly algorithm is improved herein Two kinds of heuristic information are proposed into the algorithm to guide the convergence of the algorithm The first one takes the current global best as the heuristic information referencing the “global optimal” idea in particle swarm optimization, therefore, an algorithm called FAGO (Firefly Algorithm based on Global Optimization) is formed The second one is called FABE (Firefly Algorithm based on Bayesian Estimation) using the optimal moving direction calculated by Bayesian estimation as heuristic information The improved algorithms in this study are applied to numerical simulations of several classical test functions and compared with traditional FA and some other′s research are carried out The simulation results show that the proposed algorithms can well accelerate the convergence speed and improve the convergence accuracy

参考文献/References:

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[6]ABDULLAH A,DERIS S, MOHAMAD M S, et al. A New Hybrid Firefly Algorithm for Complex and Nonlinear Problem[C]// Berlin, Heidelberg, Springer, 2012: 673.
[7]YANG X S,NatureInspired Optimization Algorithms[M]. Amsterdam, Elsevier Science Publishers, 2014.
[8]〖JP3〗CHENG S, LU H, LEI X, et al. A Quarter Century of Particle Swarm Optimization[J]. Complex & Intelligent Systems, 2018,1(3): 1. 〖JP〗
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备注/Memo

备注/Memo:
 基金项目:国家自然科学基金(61673098,61603034);北京市自然科学基金(3182027);北京市重点学科共建项目(XK100080537)
更新日期/Last Update: 2019-03-26