Distributed Simulated Annealing with MapReduce

Authors

Atanas Radenski, Chapman UniversityFollow

Files

Description

Simulated annealing’s high computational intensity has stimulated researchers to experiment with various parallel and distributed simulated annealing algorithms for shared memory, message-passing, and hybrid-parallel platforms. MapReduce is an emerging distributed computing framework for large-scale data processing on clusters of commodity servers; to our knowledge, MapReduce has not been used for simulated annealing yet. In this paper, we investigate the applicability of MapReduce to distributed simulated annealing in general, and to the TSP in particular. We (i) design six algorithmic patterns of distributed simulated annealing with MapReduce, (ii) instantiate the patterns into MR implementations to solve a sample TSP problem, and (iii) evaluate the solution quality and the speedup of the implementations on a cloud computing platform, Amazon’s Elastic MapReduce. Some of our patterns integrate simulated annealing with genetic algorithms. The paper can be beneficial for those interested in the potential of MapReduce in computationally intensive nature-inspired methods in general and simulated annealing in particular.

ISBN

978-3-642-29177-7

Publication Date

2012

Publisher

Springer

City

Berlin

Keywords

simulated annealing, MapReduce, traveling salesperson (TSP)

Disciplines

Computer Sciences

Comments

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-29178-4_47.

Recommended Citation

Radenski A. Distributed Simulated Annealing with MapReduce. In Proceedings of the 2012 European conference on Applications of Evolutionary Computation (EvoApplications'12), Cecilia Chio, Alexandros Agapitos, Stefano Cagnoni, Carlos Cotta, and Francisco Fernández Vega (Eds.). Springer-Verlag, Berlin, Heidelberg, 466-476. doi: 10.1007/978-3-642-29178-4_47.

Peer Reviewed

1

Copyright

Springer