Constrained global optimization: algorithms and applications
Constrained global optimization: algorithms and applications
ACM Transactions on Mathematical Software (TOMS)
Discrete optimization
Global optimization
Measuring parallel processor performance
Communications of the ACM
Linear-space best-first search
Artificial Intelligence
Terminal Repeller Unconstrained Subenergy Tunneling (TRUST) for fast global optimization
Journal of Optimization Theory and Applications
The annealing evolution algorithm as function optimizer
Parallel Computing
Enhanced simulated annealing for globally minimizing functions of many-continuous variables
ACM Transactions on Mathematical Software (TOMS)
Parallel Genetic Simulated Annealing: A Massively Parallel SIMD Algorithm
IEEE Transactions on Parallel and Distributed Systems
Swarm intelligence
Trace-Based Methods for Solving Nonlinear Global Optimization and Satisfiability Problems
Journal of Global Optimization
A Hybrid Genetic Algorithm for Nonconvex Function Minimization
Journal of Global Optimization
Parallel Simulated Annealing Algorithms in Global Optimization
Journal of Global Optimization
A novel metaheuristics approach for continuous global optimization
Journal of Global Optimization
Parallel Simulated Annealing using Speculative Computation
IEEE Transactions on Parallel and Distributed Systems
Sudoku using parallel simulated annealing
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
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We implemented five conversions of simulated annealing (SA) algorithm from sequential-to-parallel forms on high-performance computers and applied them to a set of standard function optimization problems in order to test their performances. According to the experimental results, we eventually found that the traditional approach to parallelizing simulated annealing, namely, parallelizing moves in sequential SA, difficultly handled very difficult problem instances. Divide-and-conquer decomposition strategy used in a search space sometimes might find the global optimum function value, but it frequently resulted in great time cost if the random search space was considerably expanded. The most effective way we found in identifying the global optimum solution is to introduce genetic algorithm (GA) and build a highly hybrid GA+SA algorithm. In this approach, GA has been applied to each cooling temperature stage. Additionally, the performance analyses of the best algorithm among the five implemented algorithms have been done on the IBM Beowulf PCs Cluster and some comparisons have been made with some recent global optimization algorithms in terms of the number of functional evaluations needed to obtain a global minimum, success rate and solution quality.