结合元胞自动机的果蝇优化算法

doi:10.11772/j.issn.1001-9081.2014.08.2295

计算机应用 ›› 2014, Vol. 34 ›› Issue (8): 2295-2298.DOI: 10.11772/j.issn.1001-9081.2014.08.2295

结合元胞自动机的果蝇优化算法

贺智明,宋建国,梅宏标

江西理工大学信息工程学院，江西赣州341000

收稿日期:2014-02-27 修回日期:2014-04-16 出版日期:2014-08-01 发布日期:2014-08-10
通讯作者: 宋建国
作者简介:贺智明(1966-)，男，江西永新人，教授，硕士，主要研究方向：数据仓库、数据挖掘；宋建国(1990-)，男，内蒙古赤峰人，硕士研究生，主要研究方向：云计算、群体智能；梅宏标(1976-)，男，江西南昌人，副教授，博士，主要研究方向：大规模仿真系统工程。
基金资助:
江西省教育厅自然科学基金资助项目;江西省研究生创新基金资助项目

Fruit fly optimization algorithm based on cellular automata

HE Zhiming,SONG Jianguo,MEI Hongbiao

Faculty of Information Engineering, Jiangxi University of Science and Technology, Ganzhou Jiangxi 341000, China

Received:2014-02-27 Revised:2014-04-16 Online:2014-08-01 Published:2014-08-10
Contact: SONG Jianguo
Supported by:
Natural science fund project in jiangxi province department of education;Graduate student innovation fund project in jiangxi province

摘要/Abstract

摘要：

果蝇优化算法(FOA)作为一类新的优化搜索算法，广泛应用于各种优化问题。针对该算法后期求解精度低、容易陷入局部最优且收敛缓慢的缺点，提出一种结合元胞自动机的果蝇优化算法(CAFOA)。该算法在首次求解时利用元胞演化规则选择果蝇最优个体邻域，然后对选择后的果蝇个体位置进行随机扰动，分别用邻域个体复制更新演化前个体位置，再次进行迭代寻优，从而有效克服算法陷入局部最优。对6种常见测试函数进行了运算仿真。实验结果表明，所提算法比传统算法的平均收敛精度提高10%，达到稳定全局最优值的平均迭代次数减少870次，从而论证了算法的有效性。

Abstract:

The Fruit fly Optimization Algorithm (FOA) was widely used in all kinds of optimization problems as a new kind of optimization search algorithm. In order to overcome the shortcomings of low precision, easily trapping in local optimum and the slow convergence in later period, a novel algorithm of FOA based on Cellular Automata (CAFOA) was proposed. CAFOA used cellular evolution rules to select the best individual drosophila neighborhood during the first evolution, then it selected the location of individual fruit fly to conduct random perturbation and replaced the previous location before evolution with its neigborhood's, so it could obtain the value of secondary optimization, jump out of local extremum and continue to optimize. Experiments were conducted on the six kinds of classical test functions for operation simulation. The experimental results show that, the average convergence precision of the proposed algorithm is 10% higher than the traditional algorithm's and the average number of iterations to achieve stable global optimal values is reduced to 870, which demonstrates the effectiveness of the new algorithm.

中图分类号:

TP301.6
TP18

贺智明宋建国梅宏标. 结合元胞自动机的果蝇优化算法[J]. 计算机应用, 2014, 34(8): 2295-2298.

HE Zhiming SONG Jianguo MEI Hongbiao. Fruit fly optimization algorithm based on cellular automata[J]. Journal of Computer Applications, 2014, 34(8): 2295-2298.

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结合元胞自动机的果蝇优化算法

Fruit fly optimization algorithm based on cellular automata

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