Distributed Simulated Annealing with MapReduce

  • Atanas Radenski
Conference paper

DOI: 10.1007/978-3-642-29178-4_47

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7248)
Cite this paper as:
Radenski A. (2012) Distributed Simulated Annealing with MapReduce. In: Di Chio C. et al. (eds) Applications of Evolutionary Computation. EvoApplications 2012. Lecture Notes in Computer Science, vol 7248. Springer, Berlin, Heidelberg

Abstract

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.

Keywords

simulated annealing MapReduce traveling salesperson (TSP) 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Atanas Radenski
    • 1
  1. 1.Chapman UniversityOrangeUSA

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