A hybrid ant colony optimization algorithm based on MapReduce

DOI

10.2991/icmemtc-16.2016.26How to use a DOI?

Keywords

MapReduce; ant colony optimization algorithm; Hadoop; Traveling salesman problem; hybrid ACO

Abstract

The traditional ant colony optimization(ACO) had defects in long searching time and convergence in local optima solution. To solve the problem, a hybrid ant colony optimization algorithm(HACO) was proposed in this paper. Two modes in HACO are defined that are DefaultModel and EliteModel. In order to search the optimum result, the two modes will be automatically switched in this algorithm. It also use the parallel calculation model of MapReduce to loop iteration part of ACO and deploy it on the Hadoop cloud computing platform. Finally, simulation results validate the proposed approach on the traveling salesman problem.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Ming Cai
AU  - Yongan Zuo
PY  - 2016/04
DA  - 2016/04
TI  - A hybrid ant colony optimization algorithm based on MapReduce
BT  - Proceedings of the 2016 3rd International Conference on Materials Engineering, Manufacturing Technology and Control
PB  - Atlantis Press
SP  - 136
EP  - 140
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmemtc-16.2016.26
DO  - 10.2991/icmemtc-16.2016.26
ID  - Cai2016/04
ER  -