启发信息引导的改进萤火虫算法  

Improved Firefly Algorithm Based on Heuristic Information

作　　者：崔家瑞[1] 李擎[1] 杨柳祎 王恒 张博钰 CUI Jia-rui;LI Qing;YANG Liu-yi;WANG Heng;ZHANG Bo-yu(School of Automation and Electrical Engineering,University of Science and Technology Beijing,Beijing 100083,China)

机构地区：[1]北京科技大学自动化学院,北京100083

出　　处：《哈尔滨理工大学学报》2019年第1期92-98,共7页Journal of Harbin University of Science and Technology

基　　金：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)

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。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.

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 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]