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Algorithms 2015, 8(3), 407-414; doi:10.3390/a8030407

Implementation of a Parallel Algorithm Based on a Spark Cloud Computing Platform

1,2
,
1,2,* and 1,2
1
School of Mechanical Engineering, Shandong University, Jinan 250061, China
2
Key Laboratory of High-efficiency and Clean Mechanical Manufacture, Shandong University, Ministry of Education, Jinan 250061, China
*
Author to whom correspondence should be addressed.
Academic Editor: Henning Fernau
Received: 19 March 2015 / Revised: 1 June 2015 / Accepted: 23 June 2015 / Published: 3 July 2015
View Full-Text   |   Download PDF [741 KB, uploaded 3 July 2015]   |  

Abstract

Parallel algorithms, such as the ant colony algorithm, take a long time when solving large-scale problems. In this paper, the MAX-MIN Ant System algorithm (MMAS) is parallelized to solve Traveling Salesman Problem (TSP) based on a Spark cloud computing platform. We combine MMAS with Spark MapReduce to execute the path building and the pheromone operation in a distributed computer cluster. To improve the precision of the solution, local optimization strategy 2-opt is adapted in MMAS. The experimental results show that Spark has a very great accelerating effect on the ant colony algorithm when the city scale of TSP or the number of ants is relatively large. View Full-Text
Keywords: cloud computing; MAX-MIN Ant System; TSP; MapReduce; Spark platform cloud computing; MAX-MIN Ant System; TSP; MapReduce; Spark platform
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Wang, L.; Wang, Y.; Xie, Y. Implementation of a Parallel Algorithm Based on a Spark Cloud Computing Platform. Algorithms 2015, 8, 407-414.

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