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Open AccessArticle

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

by 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
Algorithms 2015, 8(3), 407-414; https://doi.org/10.3390/a8030407
Received: 19 March 2015 / Revised: 1 June 2015 / Accepted: 23 June 2015 / Published: 3 July 2015
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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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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