Abstract
Ant colony optimization algorithms have been successfully applied to solve many problems. However, in some large scale optimization problems involving large amounts of data, the optimization process may take hours or even days to get an excellent solution. Developing parallel optimization algorithms is a common way to tackle with this issue. In this paper, we present a MapReduce Max-Min Ant System (MRMMAS), a MMAS implementation based on the MapReduce parallel programming model. We describe MapReduce and show how MMAS can be naturally adapted and expressed in this model, without explicitly addressing any of the details of parallelization. We present benchmark travelling salesman problems for evaluating MRMMAS. The experimental results demonstrate that the proposed algorithm can scale well and outperform the traditional MMAS with similar running times.
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Tan, Q., He, Q., Shi, Z. (2012). Parallel Max-Min Ant System Using MapReduce. In: Tan, Y., Shi, Y., Ji, Z. (eds) Advances in Swarm Intelligence. ICSI 2012. Lecture Notes in Computer Science, vol 7331. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30976-2_22
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DOI: https://doi.org/10.1007/978-3-642-30976-2_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30975-5
Online ISBN: 978-3-642-30976-2
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