Abstract
Simulated annealing is known to be an efficient method for combinatorial optimization problems. Its usage for realistic problem size, however, has been limited by the long execution time due to its sequential nature. This report presents a practical approach to synchronous simulated annealing for massively parallel distributed-memory multiprocessors. We use an n-ary speculative tree to execute n different iterations in parallel on n processors, called Generalized Speculative Computation (GSC). Execution results of the 100- to 500-city Traveling Salesman Problems on the AP1000 massively parallel multiprocessor demonstrate that the GSC approach can be an effective method for parallel simulated annealing as it gave over 20-fold speedup on 100 processors.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 997-1005 |
| Number of pages | 9 |
| Journal | IEEE Transactions on Parallel and Distributed Systems |
| Volume | 6 |
| Issue number | 10 |
| DOIs | |
| State | Published - Oct 1995 |
All Science Journal Classification (ASJC) codes
- Signal Processing
- Hardware and Architecture
- Computational Theory and Mathematics
Keywords
- Parallel simulated annealing
- Traveling Salesman Problem
- combinatorial optimization
- simulated annealing
- speculative computation
- synchronous simulated annealing