A parallel simulated annealing algorithm
Parallel Computing
Synchronous and Asynchronous Parallel Simulated Annealing with Multiple Markov Chains
IEEE Transactions on Parallel and Distributed Systems
Parallel Simulated Annealing: Accuracy vs. Speed in Placement
IEEE Design & Test
Parallel N-ary Speculative Computation of Simulated Annealing
IEEE Transactions on Parallel and Distributed Systems
Parallel Simulated Annealing: An Adaptive Approach
IPPS '97 Proceedings of the 11th International Symposium on Parallel Processing
A parallel local-search algorithm for the k-partitioning problem
HICSS '95 Proceedings of the 28th Hawaii International Conference on System Sciences
Massively Parallel Simulated Annealing Embedded with Downhill A SPMD Algorithm for Cluster Computing
IWCC '99 Proceedings of the 1st IEEE Computer Society International Workshop on Cluster Computing
Parallel adaptive simulated annealing for computer-aided measurement in functional MRI analysis
Expert Systems with Applications: An International Journal
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Simulated annealing algorithm with adaptive neighborhood
Applied Soft Computing
Hierarchical parallel simulated annealing and its applications
ICA3PP'05 Proceedings of the 6th international conference on Algorithms and Architectures for Parallel Processing
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Simulated annealing (SA) is a stochastic optimization technique which guarantees under certain conditions to converge to a global minimum. The major disadvantage of this technique is its very slow convergence: this makes it not suitable for many complex optimization problems. Different parallel versions of the algorithm have been proposed, but none of them addresses recent 2-way symmetric multiprocessor (SMP) machines. In this paper, we present a novel approach to the parallel implementation of SA on an SMP system. In addition, we offer an adaptive method to dynamically change the program execution flow at run time, as to obtain the maximum benefit from these shared memory parallel architectures. Since we only exploit time measures for this purpose, we obtain a problem independent and a general purpose implementation. The effectiveness of the method is demonstrated by extensively analyzing the traveling salesman problem (TSP) as a target case study, on a system under different workload conditions.