Implementation and Analysis of Iterative MapReduce Based Heuristic Algorithm for Solving N-Puzzle

被引：0

作者：

Kondekar, Rohit P. ^{[1
]}

Modi, Mohit ^{[1
]}

Gupta, Akash ^{[1
]}

Deshpande, Parag S. ^{[2
]}

Saluja, Gulshan ^{[2
]}

Maru, Richa ^{[2
]}

Rokde, Ankit ^{[2
]}

机构：

[1] Visvesvaraya Natl Inst Technol, Comp Sci & Engn, Natl Inst Technol, Nagpur, Maharashtra, India

[2] Visvesvaraya Natl Inst Technol, Nagpur, Maharashtra, India

来源：

JOURNAL OF COMPUTERS | 2014年 / 9卷 / 02期

关键词：

hadoop; heuristic; n-puzzle; parallel breadth first heuristic search; mapreduce;

D O I：

10.4304/jcp.9.2.420-424

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

MapReduce Programming paradigm provides an elegant and efficacious platform for catering large scale parallel implementations of Heuristic Search Algorithms. We present here an implementation and analysis of Parallel Breadth First Heuristic Search (PBFHS) Algorithm for solving very large combinatorial problems. Using N-Puzzle as our application domain we found that the scalability of Breadth First Search (BFS) and Iterative Deepening A* (IDA*) is limited on a single machine due to hardware constraints. In this algorithm, we generate a remarkably restrictive, yet a large search space using combination of highly efficient admissible and non-admissible heuristics. The graphs compiled from resulting output advocates our design and implementation flow. A 7 node Hadoop cluster setup on Amazon EC2, solves the hardest 24 Puzzle in 3 hours, and 35 Puzzle in 13 hours of computing time.

引用

页码：420 / 424

页数：5

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