Future paths for integer programming and links to artificial intelligence
Computers and Operations Research - Special issue: Applications of integer programming
ACO algorithms for the quadratic assignment problem
New ideas in optimization
Computers and Intractability; A Guide to the Theory of NP-Completeness
Computers and Intractability; A Guide to the Theory of NP-Completeness
A Template for Scatter Search and Path Relinking
AE '97 Selected Papers from the Third European Conference on Artificial Evolution
Multistart tabu search and diversification strategies for the quadratic assignment problem
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Paper: Robust taboo search for the quadratic assignment problem
Parallel Computing
Tabu Search with two approaches to parallel flowshop evaluation on CUDA platform
Journal of Parallel and Distributed Computing
Fitness landscape analysis and memetic algorithms for the quadratic assignment problem
IEEE Transactions on Evolutionary Computation
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NVidia's powerful GPU hardware and CUDA platform enables the design of very fast parallel algorithms. Relatively little research has been done so far on GPU implementations of algorithms for computationally demanding discrete optimisation problems. In this paper, the well-known NP-hard Quadratic Assignment Problem (QAP) is considered. Detailed analysis of parallelisation possibilities, memory organisation and access patterns, enables the implementation of fast and effective heuristics for QAP on the GPU - the Parallel Multistart Tabu Search (PMTS). Computational experiments show that PMTS is capable of providing good quality (often optimal or the best known) solutions in a short time, and even better quality solutions in longer runs. PMTS runs up to 420x faster than a single-core counterpart, or 70x faster than a parallel CPU implementation on a high-end six-core CPU.