Abstract
This paper presents a fine-grained parallel genetic algorithm improved with a 2-opt heuristic for finding solutions near to the optimum to the Quadratic Assignment Problem (QAP). The proposed algorithm is fully implemented on Graphics Processing Units (GPUs). Unlike previous approaches reported in the literature our implementation a two-dimensional GPU grid of size \(10\,\times \,10\) defines the population of the genetic algorithm (set of permutations of the QAP) and each GPU block consists of n GPU threads where n is the size of QAP. Each GPU block is used to represent the chromosome of a single individual and each GPU thread represents a gene of such chromosome. The proposed algorithm is tested on a subset of the standard QAPLIB data set. Our results show that our implementation is able to find good solutions for large QAP instances in few parallel iterations of the evolutionary process.
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Notes
- 1.
n columns and 100 rows, contrary to the orders of matrices of linear algebra.
References
Sahni, S., Gonzalez, T.: P-complete approximation problems. J. Assoc. 23(3), 555–565 (1976)
Gilmore, P.: Optimal and suboptimal algorithms for the quadratic assignment problem. SIAM J. Appl. Math. 10, 305–313 (1962)
Balas, E., Mazzola, J.: Nonlinear programming: I. Linearization techniques. Math. Program. 30, 1–21 (1984)
Lawler, E.: The quadratic assignment problem. Manag. Sci. 9, 586–599 (1963)
Murthy, K., Pardalos, P., Li, Y.: A local search algorithm for the quadratic assignment problem. Informatica 3, 524–538 (1992)
Li, Y., Pardalos, M., Resende, M.: A greedy randomized adaptive search procedure for the quadratic assignment problem. In: Quadratic Assignment and Related Problems. DIMACS Series in Discrete Mathematics and Theoretical Computer Science, vol. 16, pp. 237–261 (1994)
QAPLIB (2017). http://anjos.mgi.polymtl.ca/qaplib/
Tomassini, M.: A survey of genetic algorithms. In: Annual Reviews of Computational Physics, vol. III, pp. 87–118. World Scientific (1995)
NVIDIA CUDA (2016). https://developer.nvidia.com/cuda-gpus
Burkard, R., Cela, E., Pardalos, P., Pitsoulis, P.: The quadratic assignment problem, 241–3388 (1998)
Cantú Paz, E.: Survey of parallel genetic algorithms. Technical report 97003, Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana Champaign (1997)
Taillard, E.: Comparision of Iterative Searches for the Quadratic Assignment Problem. Montreal, Canada (1994)
Ujaldon, M.: Programming GPUs with CUDA, tutorial. In: 18th IEEE CSE 2015 and 13th IEEE EUC 2015 Conferences, Porto, Portugal (2015)
Mohammadi, J., Mirzaie, K., Derhami, V.: Parallel genetic algorithm based on GPU for solving quadratic assignment problem. In: 2nd International Conference on Knowledge-Based Engineering and Innovation (KBET), Tehran, Iran. IEEE (2015)
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Poveda, R., Gómez, J. (2018). Solving the Quadratic Assignment Problem (QAP) Through a Fine-Grained Parallel Genetic Algorithm Implemented on GPUs. In: Nguyen, N., Pimenidis, E., Khan, Z., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2018. Lecture Notes in Computer Science(), vol 11056. Springer, Cham. https://doi.org/10.1007/978-3-319-98446-9_14
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DOI: https://doi.org/10.1007/978-3-319-98446-9_14
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