[1]牛勇力[],吴清[][],李平娜[],等. 自适应修改权重参数的果蝇优化算法[J].计算机技术与发展,2017,27(11):41-45.
　NIU Yong-li[],WU Qing[][],LI Ping-na[],et al. A Fruit Fly Optimization Algorithm with Adaptive Modifying Weight Parameters[J].,2017,27(11):41-45.
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 自适应修改权重参数的果蝇优化算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
27
期数:
2017年11期
页码:
41-45
栏目:
智能、算法、系统工程
出版日期:
2017-11-10

文章信息/Info

Title:
 A Fruit Fly Optimization Algorithm with Adaptive Modifying Weight Parameters
文章编号:
1673-629X（2017)11-0041-05
作者:
 牛勇力[1] ; 吴清[1][2] ; 李平娜[1] ; 谢章华[1]
 1.河北工业大学 计算机科学与软件学院；2.河北省大数据计算重点实验室
Author(s):
 NIU Yong-li[1];  WU Qing[1][2] ; LI Ping-na[1];  XIE Zhang-hua[1]
关键词:
 ; 果蝇优化算法; 自适应; 迭代步长; 认知因子; 引导因子
Keywords:
 ; fruit fly optimization algorithm; adaptive; iterative step; cognitive factor; guiding factor
分类号:
TP18；TP301
文献标志码:
A
摘要:
 
针对果蝇优化算法容易陷入局部极值、迭代后期收敛速度慢和收敛精度低的缺陷,借鉴粒子群优化算法中个体认知因子和群体引导因子的思想,提出了自适应修改权重参数的果蝇优化算法.该算法引入了果蝇个体认知因子和果蝇群体引导因子,让果蝇个体对自己的位置有充分的认知,也让果蝇群体对果蝇个体有很好的引导.在每次迭代时,算法根据当前果蝇群体的适应度值自适应修改个体认知因子和群体引导因子的大小,从而调整迭代步长的大小,使得改进后的算法能够避免早熟收敛,提高收敛精度和收敛速度.基于测试函数的实验结果表明,自适应修改权重参数的果蝇优化算法能跳出局部极值,具有更好的全局搜索能力,在收敛速度和收敛精度方面比基本果蝇优化算法有较大的提高.
Abstract:
 In view of the defects of easily falling into local extreme,slow convergence speed in later iteration and low convergence preci-sion for fruit fly optimization algorithm,a fruit fly optimization algorithm based on adaptive modifying weight parameters is proposed considering individual cognitive factor and group guiding factor of particle swarm optimization. It introduces individual cognitive factor and group guiding factor so that the individual has sufficient awareness on its own position and the group has a good guide on the individ-uals. In each loop of iteration it dynamically has modified size of cognitive factor and guiding factor based on the current value of fitness of the fruit fly group and regulated iterative step size by adaptive method,which makes it avoid the premature convergence and improve its convergence accuracy and convergence rate. Experimental results of standard test functions show that it can jump out of local extreme with advantages of more precise and faster convergence.

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更新日期/Last Update: 2017-12-25