Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
Parallel simulated annealing techniques
CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
Parallel Genetic Simulated Annealing: A Massively Parallel SIMD Algorithm
IEEE Transactions on Parallel and Distributed Systems
Parallel N-ary Speculative Computation of Simulated Annealing
IEEE Transactions on Parallel and Distributed Systems
A Methodology to Parallel the Temperature Cycle in Simulated Annealing
MICAI '00 Proceedings of the Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
The coordination of parallel search with common components
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
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The Methodology to Parallelize Simulated Annealing (MPSA) leads to massive parallelization by executing each temperature cycle of the Simulated Annealing (SA) algorithm in parallel. The initial solution for each internal cycle is set through a Monte Carlo random sampling to adjust the Boltzmann distribution at the cycle beginning. MPSA uses an asynchronous communication scheme and any implementation of MPSA leads to a parallel Simulated Annealing algorithm that is in general faster than its sequential implementation version while the precision is held. This paper illustrates the advantages of the MPSA scheme by parallelizing a SA algorithm for the Traveling Salesman Problem.