Parallel simulated annealing for shape detection
Computer Vision and Image Understanding
Parallel simulated annealing algorithms
Journal of Parallel and Distributed Computing
A parallel variant of simulated annealing for optimizing mesh partitions on workstations
Advances in Engineering Software - Special issue; special issue on large-scale analysis and design on high-performance computers and workstations
Introduction to Parallel Computing
Introduction to Parallel Computing
Parallel Simulated Annealing Algorithms for Cell Placement on Hypercube Multiprocessors
IEEE Transactions on Parallel and Distributed Systems
Parallel N-ary Speculative Computation of Simulated Annealing
IEEE Transactions on Parallel and Distributed Systems
A methodological approach to parallel simulated annealing on an SMP System
Journal of Parallel and Distributed Computing
HPC '00 Proceedings of the The Fourth International Conference on High-Performance Computing in the Asia-Pacific Region-Volume 2 - Volume 2
Parallel Programming: Techniques and Applications Using Networked Workstations and Parallel Computers (2nd Edition)
A quantitative comparison of functional MRI cluster analysis
Artificial Intelligence in Medicine
Fuzzy cluster analysis of high-field functional MRI data
Artificial Intelligence in Medicine
An evaluation of parallel simulated annealing strategies with application to standard cell placement
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Clustering and selecting suppliers based on simulated annealing algorithms
Computers & Mathematics with Applications
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Simulated annealing (SA) is known to be one of the most efficient heuristic algorithms for complex nonlinear optimization problems. However, it suffers from the large amount of computing time required to obtain a near-optimal solution. To overcome this problem, a parallel version of the algorithm is worthy of evaluation. This report presents a Parallel Adaptive Simulated Annealing (PASA) algorithm for computer-aided measurement in locating the activation area of functional magnetic resonance images (fMRI). The parallel paradigm is based on a coarse-grained (or island) model performed on a cluster of PCs by using a message-passing interface (MPI) for the information interchange. Performance of this approach is evaluated by computing the receiver operating characteristic (ROC) area and Jaccard similarity. Experimental results show that the coarse-grained PASA outperforms other approaches and can also efficiently and consistently extract activities with different contrast-to-noise ratios and activation-area sizes.