Parallel simulated annealing algorithms
Journal of Parallel and Distributed Computing
Efficient and Accurate Parallel Genetic Algorithms
Efficient and Accurate Parallel Genetic Algorithms
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Studies in Computational Science: Parallel Programming Paradigms
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Scaling Genetic Algorithms Using MapReduce
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Fast parallelization of differential evolution algorithm using MapReduce
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Data-Intensive Text Processing with MapReduce
Data-Intensive Text Processing with MapReduce
Scaling Populations of a Genetic Algorithm for Job Shop Scheduling Problems Using MapReduce
CLOUDCOM '10 Proceedings of the 2010 IEEE Second International Conference on Cloud Computing Technology and Science
Parallelizing Simulated Annealing-Based Placement Using GPGPU
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PVM/MPI'05 Proceedings of the 12th European PVM/MPI users' group conference on Recent Advances in Parallel Virtual Machine and Message Passing Interface
Speeding-up codon analysis on the cloud with local MapReduce aggregation
Information Sciences: an International Journal
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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.