A hybrid ant colony optimization algorithm based on MapReduce

被引：0
|
作者：
Cai, Ming [1 ]
Zuo, Yongan [1 ]
机构：
[1] Jiangnan Univ, Sch Internet Things Engn, Wuxi 214122, Peoples R China
来源：
PROCEEDINGS OF THE 2016 3RD INTERNATIONAL CONFERENCE ON MATERIALS ENGINEERING, MANUFACTURING TECHNOLOGY AND CONTROL | 2016年 / 67卷
关键词：
MapReduce; ant colony optimization algorithm; Hadoop; Traveling salesman problem; hybrid ACO;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The traditional ant colony optimization (ACO) had defects in long searching time and convergence in local optima solution. To solve the problem, a hybrid ant colony optimization algorithm (HACO) was proposed in this paper. Two modes in HACO are defined that are DefaultModel and EliteModel. In order to search the optimum result, the two modes will be automatically switched in this algorithm. It also use the parallel calculation model of MapReduce to loop iteration part of ACO and deploy it on the Hadoop cloud computing platform. Finally, simulation results validate the proposed approach on the traveling salesman problem.
引用
收藏
页码：136 / 140
页数：5
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