[1]刘立群[],韩俊英[],代永强[],等. 果蝇优化算法优化性能对比研究[J].计算机技术与发展,2015,25(08):94-98.
　LIU Li-qun[],HAN Jun-ying[],DAI Yong-qiang[],et al. Comparative Study on Optimization Performance of Fruit Fly Optimization Algorithm [J].,2015,25(08):94-98.
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 果蝇优化算法优化性能对比研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
25
期数:
2015年08期
页码:
94-98
栏目:
智能、算法、系统工程
出版日期:
2015-08-10

文章信息/Info

Title:
 Comparative Study on Optimization Performance of Fruit Fly Optimization Algorithm

文章编号:
1673-629X（2015)08-0094-05
作者:
 刘立群[1]; 韩俊英[1]; 代永强[1]; 火久元[2]
 1.甘肃农业大学 信息科学技术学院；2.兰州交通大学 电子与信息工程学院
Author(s):
 LIU Li-qun[1] ; HAN Jun-ying[1] ; DAI Yong-qiang[1] ; HUO Jiu-yuan[2]
关键词:
 ; 群体智能优化算法; 果蝇优化算法; 混合蛙跳算法; 和声搜索算法; 人工蜂群算法; 优化性能
Keywords:
 ; swarm intelligence optimization algorithm; fruit fly optimization algorithm; shuffled frog leaping algorithm ; harmony searchalgorithm;  artificial bee colony algorithm; optimization performance
分类号:
TP301.6
文献标志码:
A
摘要:
针对群体智能优化算法自身的特点和优势，分析对比了果蝇优化算法、混合蛙跳算法、和声搜索算法和人工蜂群算法四种智能优化算法的优化性能。以最新提出的果蝇优化算法为基准，与其他三种智能优化算法进行优化性能的横向对比实验。实验结果表明，与其他三种算法相比，果蝇优化算法具有参数少、全局寻优能力强、收敛速度快等特点，在进化次数较低时，其收敛精度和速度最高，但是随着进化次数的增大，存在容易收敛到局部最优值，收敛速度慢，在求解部分单峰值和多峰值函数优化问题时优化效果不理想等缺陷。果蝇优化算法尚需加强其理论改进，以提高其搜索的质量和效率，为群体智能优化算法的融合和改进技术提供重要支持。
Abstract:
 Aiming at the characteristic and advantage of swarm intelligence optimization algorithm,a comparative study on optimization performance of four swarm intelligence algorithms including fruit fly optimization algorithm, shuffled frog leaping algorithm, harmony search algorithm and artificial bee colony algorithm is proposed. Compared with the other three algorithms,the results indicate that fruit fly optimization algorithm has some characteristics such as few parameters, high ability of global optimization and rapid convergence speed. In the lower evolution times,the convergence precision and speed of fruit fly optimization algorithm is the best of four algorithms. But with the increasing times of evolution,the fruit fly optimization algorithm exists some limitations such as easy to local convergence, slower computing speed,and not satisfactory in solving the optimization problem of little function. It is necessary to strengthen its theory of fruit fly optimization algorithm to improve its quality and searching efficiency,which provides important support for the integration and improvement technology of swarm intelligence optimization algorithm.

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更新日期/Last Update: 2015-09-11