A Parallel Genetic Algorithm for Solving the Probabilistic Minimum Spanning Tree Problem

被引：0
|
作者：
Wang, Zhurong [1 ]
Yu, Changqing [1 ]
Hei, Xinhong [1 ]
Zhang, Bin [1 ]
机构：
[1] Xian Univ Technol, Sch Engn & Comp Sci, Xian, Peoples R China
来源：
2013 9TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND SECURITY (CIS) | 2013年
关键词：
Parallel genetic algorithm; Probabilistic minimum spanning tree; Determinant factorization encoding; Message passing interface (MPI);
D O I：
10.1109/CIS.2013.20
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The probabilistic minimum spanning tree (PMST) problem is NP-complete and is hard to solve. However, it has important theoretical significance and wide application prospect. A parallel genetic algorithm based on coarse-grained model is proposed to solve PMST problem in this paper. Firstly, we discuss several problems of determinant factorization encoding, and develop repairing method for illegal individuals. Secondly, a coarsegrained parallel genetic algorithm, which combines message passing interface (MPI) and genetic algorithm, is designed to solve probabilistic minimum spanning tree problems. Finally, the proposed algorithm is used to test several probabilistic minimum spanning tree problems which are generated by the method introduced in the literature. The statistical data of the test results show that the expectation best solution and average best solution obtained by the proposed algorithm are better than those provided in the literature.
引用
收藏
页码：61 / 65
页数：5
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