Computational Optimization and Applications
Performance analysis for parallel solutions to generic search problems
SAC '97 Proceedings of the 1997 ACM symposium on Applied computing
State of the Art in Parallel Search Techniques for Discrete Optimization Problems
IEEE Transactions on Knowledge and Data Engineering
Control Schemes in a Generalized Utility for Parallel Branch-and-Bound Algorithms
IPPS '97 Proceedings of the 11th International Symposium on Parallel Processing
RAT: RC Amenability Test for Rapid Performance Prediction
ACM Transactions on Reconfigurable Technology and Systems (TRETS)
A framework for multi-robot node coverage in sensor networks
Annals of Mathematics and Artificial Intelligence
Solving traveling salesman problem on high performance computing using message passing interface
CIMMACS'08 Proceedings of the 7th WSEAS international conference on Computational intelligence, man-machine systems and cybernetics
Solving traveling salesman problem on cluster compute nodes
WSEAS Transactions on Computers
Performance metrics and evaluation of a path planner based on genetic algorithms
PerMIS '07 Proceedings of the 2007 Workshop on Performance Metrics for Intelligent Systems
Combining multi-core and GPU computing for solving combinatorial optimization problems
Journal of Parallel and Distributed Computing
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This paper is the first to present a parallelization of a highly efficient best-first branch-and-bound algorithm to solve large symmetric traveling salesman problems on a massively parallel computer containing 1024 processors. The underlying sequential branch-and-bound algorithm is based on 1-tree relaxation. The parallelization of the branch-and-bound algorithm is fully distributed. Every processor performs the same sequential algorithm but on a different part of the solution tree. To distribute subproblems among the processors we use a new direct-neighbor dynamic load-balancing strategy. The general principle can be applied to all other branch-and-bound algorithms leading to an "automatic" parallelization. At present we can efficiently solve traveling salesman problems up to a size of 318 cities on networks of up to 1024 transputers. On hard problems we achieve an almost linear speed-up.