Abstract
With the proliferation of graphics processing units (GPU) supporting general-purpose computing (GPGPU), many computationally demanding applications are being redesigned to exploit the capabilities offered by massively parallel computing platforms. This paper presents a Bees Algorithm (BA) for the Quadratic Assignment Problem (QAP) implemented on the CUDA platform. The motivations for our work were twofold: firstly, we wanted to develop a dedicated algorithm to solve the QAP showing both time and optimization performance, secondly, we planned to check if the capabilities offered by popular GPUs can be exploited to accelerate hard optimization tasks requiring high computational power. The paper describes both sequential and parallel algorithm implementations, as well as reports results of tests.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bansal, J.C., Sharma, H., Nagar, A., Arya, K.V.: Balanced artificial bee colony algorithm. Int. J. Artif. Intell. Soft Comput. 3(3), 222–243 (2013)
Bermudez, R., Cole, M.H.: A genetic algorithm approach to door assignments in breakbulk terminals. Technical Report MBTC-1102, Mack-Blackwell Transportation Center, University of Arkansas, Fayetteville, Arkansas (2001)
Burkard, R., Karisch, S., Rendl, F.: QAPLIB—a quadratic assignment problem library. J. Glob. Optim. 10(4), 391–403 (1997)
Chakrapani, J., Skorin-Kapov, J.: Massively parallel tabu search for the quadratic assignment problem. Ann. Oper. Res. 41(4), 327–341 (1993)
Chmiel, W.: Evolution Algorithms for optimisation of task assignment problem with quadratic cost function. Ph.D. thesis, AGH Technology University, Kraków, Poland (2004)
Chmiel, W., Kadłuczka, P., Packanik, G.: Performance of swarm algorithms for permutation problems. Automatyka 15(2), 117–126 (2009)
Chong, C.S., Sivakumar, A.I., Low, M.Y.H., Gay, K.L.: A bee colony optimization algorithm to job shop scheduling. In: WSC 2006. Monterey, USA (2006)
Grötschel, M.: Discrete mathematics in manufacturing. In: Malley, R.E.O. (ed.) ICIAM 1991, pp. 119–145 (1991)
Huang, Y.M., Lin, J.C.: A new bee colony optimization algorithm with idle-time-based filtering scheme for open shop-scheduling problems. Expert Syst. Appl. 38(5), 5438–5447 (2011)
jcuda.org: JCuda–Java bindings for CUDA (2015), http://www.jcuda.org
Kirk, D.B., Hwu, W.M.: Programming Massively Parallel Processors: A Hands-on Approach, 1st edn. Morgan Kaufmann Publishers, San Francisco, USA (2010)
Koopmans, T.C., Beckmann, M.J.: Assignment problems and the location of economic activities. Econometrica 25, 53–76 (1957)
Krüger, F., Maitre, O., Jiménez, S., Baumes, L.A., Collet, P.: Generic local search (memetic) algorithm on a single GPGPU chip. In: Tsutsui, S., Collet, P. (eds.) Massively Parallel Evolutionary Computation on GPGPUs, pp. 63–81. Natural Computing Series, Springer, Berlin (2013)
Luo, G.H., Huang, S.K., Chang, Y.S., Yuan, S.M.: A parallel bees algorithm implementation on GPU. J. Syst. Arch. 60(3), 271–279 (2014)
Maitre, O.: Genetic programming on GPGPU cards using EASEA. In: Tsutsui, S., Collet, P. (eds.) Massively Parallel Evolutionary Computation on GPGPUs, pp. 227–248. Natural Computing Series, Springer, Berlin (2013)
Mason, A., Rönnqvist, M.: Solution methods for the balancing of jet turbines. Comput. Oper. Res. 24(2), 153–167 (1997)
Mirzazadeh, M., Shirdel, G.H., Masoumi, B.: A honey bee algorithm to solve quadratic assignment problem. J. Optim. Ind. Eng. 9, 27–36 (2011)
Nickolls, J., Dally, W.J.: The GPU computing era. IEEE Micro 30(2), 56–69 (2010)
NVIDIA Corporation: CUDA toolkit documentation v6.5 (2015), http://docs.nvidia.com/cuda/index.html#axzz3T4PFSm60
Owens, J.D., Luebke, D., Govindaraju, N., Harris, M., Krüger, J., Lefohn, A.E., Purcell, T.J.: A survey of general-purpose computation on graphics hardware. Comput. Graph. Forum 26(1), 80–113 (2007)
Pham, D.T., Castellani, M.: Benchmarking and comparison of nature-inspired population-based continuous optimisation algorithms. Soft Comput. 18, 1–33 (2013)
Pham, D.T., Ghanbarzadeh, A., Koc, E., Otri, S., Rahim, S., Zaidi, M.: The bees algorithm, a novel tool for complex optimisation problems. IPROMS 2006, 454–459 (2006)
Phillips, A.T., Rosen, J.B.: A quadratic assignment formulation of the molecular conformation problem. J. Glob. Optim. 4, 229–241 (1994)
Sahni, S., Gonzalez, T.: P-complete approximation problems. J. ACM 23(3), 555–565 (1976)
Szwed, P., Chmiel, W.: Multi-swarm PSO algorithm for the quadratic assignment problem: a massive parallel implementation on the OpenCL platform. Comput. Res. Repos. 1504.05158 (2015)
Szwed, P., Chmiel, W., Kadłuczka, P.: OpenCL implementation of PSO algorithm for the quadratic assignment problem. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) Artificial Intelligence and Soft Computing, LNCS, vol. 9120, pp. 223–234. Springer, Switzerland (2015)
Tadeusiewicz, R., Lewicki, A.: The ant colony optimization algorithm for multiobjective optimization non-compensation model problem staff selection. In: Cai, Z., Hu, C., Kang, Z., Liu, Y. (eds.) Advances in Computation and Intelligence, LNCS, vol. 6382, pp. 44–53. Springer, Berlin (2010)
Taillard, E.: Robust taboo search for the quadratic assignment problem. Parallel Comput.17(4–5), 443–455 (1991)
Zhou, Y., Tan, Y.: GPU-based parallel particle swarm optimization. In: IEEE CEC 2009, pp. 1493–1500. Trondheim, Norway (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Chmiel, W., Szwed, P. (2016). Bees Algorithm for the Quadratic Assignment Problem on CUDA Platform. In: Gruca, A., Brachman, A., Kozielski, S., Czachórski, T. (eds) Man–Machine Interactions 4. Advances in Intelligent Systems and Computing, vol 391. Springer, Cham. https://doi.org/10.1007/978-3-319-23437-3_53
Download citation
DOI: https://doi.org/10.1007/978-3-319-23437-3_53
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23436-6
Online ISBN: 978-3-319-23437-3
eBook Packages: EngineeringEngineering (R0)