[1]吴昌友.一种改进的人工鱼群优化算法[J].智能系统学报,2015,10(03):465-469.[doi:10.3969/j.issn.1673-4785.201404010]
 WU Changyou.An improved artificial fish swarm optimization algorithm[J].CAAI Transactions on Intelligent Systems,2015,10(03):465-469.[doi:10.3969/j.issn.1673-4785.201404010]
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一种改进的人工鱼群优化算法(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第10卷
期数:
2015年03期
页码:
465-469
栏目:
学术论文—智能系统
出版日期:
2015-06-25

文章信息/Info

Title:
An improved artificial fish swarm optimization algorithm
作者:
吴昌友
山东工商学院 管理科学与工程学院, 山东 烟台 264005
Author(s):
WU Changyou
School of Management Science and Engineering, Shandong Institute of Business And Technology, Yantai 264005, China
关键词:
人工鱼群优化算法觅食群聚追尾移动步长变异策略
Keywords:
artificial fish swarm optimization algorithmpreyswarmfollowmoving step lengthmutation strategy
分类号:
TP18
DOI:
10.3969/j.issn.1673-4785.201404010
文献标志码:
A
摘要:
对人工鱼群优化算法的觅食行为、群聚行为、追尾行为和公告板设置等基本原理进行分析,指出算法在复杂优化问题上产生初始人工鱼群难和陷入局部最优解的原因,提出了改进人工鱼群优化算法,给出了初始人工鱼群产生的方法,在人工鱼群优化算法的觅食行为、群聚行为、追尾行为中引入了自适应移动步长,同时在算法中引入变异策略,避免算法陷入局部最优,提高全局寻优能力.最后通过对4个测试函数进行实验,对于函数f1f2f4来说,虽然改进的人工鱼群算法和标准人工鱼群算法都达到了最优值,但是改进的人工鱼群算法收敛的速度更快;函数f3来说,标准人工鱼群算法运行多次都陷入最优解,无法找到全局最优解.因此,实验说明了改进算法的有效性与精确性.
Abstract:
In this paper, the basic principles of artificial fish’s behaviors of prey, swarm, follow and bulletin board set were analyzed. Investigations were conducted to explore the reasons why it is difficult to produce the initial artificial fish swarm, and why it always falls into local optional solution. The proposed solution improves the artificial fish algorithm with the method of the produce of initial artificial fish swarm, in the artificial fish’s behaviors of prey, swarm and follow introduced the adaptive mobile step length with mutation strategy into the artificial fish at the same time, avoiding fish caught in local optima, improving the ability of global optimization. Finally, through the experiment of the 4 test functions concluded that as for the function of f1, f2 and f4, while the improved artificial fish swarm algorithm and artificial fish swarm algorithm have reached the optimal value, but the convergence of the improved artificial fish swarm algorithm is faster. As to the function of f3, the standard artificial fish swarm algorithm run in to the optimal solution in several times’ operation and the global optimal solution cannot be found. Therefore, the experiment shows the effectiveness and accuracy of the improved algorithm.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2014-4-8;改回日期:。
基金项目:国家自然科学基金资助项目(71272122,71373148);山东省社科规划项目(13DGLJ05);山东能源经济协同创新中心资助项目(2014SDXT005);山东省软科学项目(2014RKB01021).
作者简介:吴昌友,男,1981年生,副教授,博士,主要研究方向为系统工程和人工智能算法.主持和参与省部级项目6项,发表学术论文30余篇,出版专著1部,主编教材1部.
通讯作者:吴昌友. E-mail: wuchangyou_81@163.com.
更新日期/Last Update: 2015-07-15