Abstract
Google Machine Reassignment Problem (GMRP) is a recent real world problem proposed at ROADEF/EURO challenge 2012. The aim of this problem is to maximise the usage of the available machines by reassigning processes among those machines while a numerous constraints must be not violated. In this work, we propose a great deluge algorithm with multi-neighbourhood operators (MNGD) for GMRP. Great deluge (GD) algorithm is a single solution based heuristic that accept non-improving solutions in order to escape from the local optimal point. The proposed algorithm uses multi-neighbourhood operators of various characteristics to effectively navigate the search space. The proposed algorithm is evaluated on a total of 30 instances. Computational results disclose that our proposed MNGD algorithm performed better than GD with single neighbourhood operator. Furthermore, MNGD algorithm obtains best results compared with other algorithms from the literature on some instances.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Roadef/euro challenge 2012: Machine reassignment. http://challenge.roadef.org/2012/en/
Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., et al.: A view of cloud computing. Commun. ACM 53(4), 50–58 (2010)
Brandt, F., Speck, J., Völker, M.: Constraint-based large neighborhood search for machine reassignment. Ann. Oper. Res. 242(1), 63–91 (2016)
Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw.: Pract. Exp. 41(1), 23–50 (2011)
Paul, P.C., Beasley, J.E.: A genetic algorithm for the multidimensional knapsack problem. J. Heuristics 4(1), 63–86 (1998)
Dueck, G.: New optimization heuristics: the great deluge algorithm and the record-to-record travel. J. Comput. Phys. 104(1), 86–92 (1993)
Gavranović, H., Buljubašić, M., Demirović, E.: Variable neighborhood search for google machine reassignment problem. Electron. Notes Discrete Math. 39, 209–216 (2012)
Lopes, R., Morais, V.W., Noronha, T.F., Souza, V.A.: Heuristics and matheuristics for a real-life machine reassignment problem. Int. Trans. Oper. Res. 22(1), 77–95 (2015)
Masson, R., Vidal, T., Michallet, J., Penna, P.H.V., Petrucci, V., Subramanian, A., Dubedout, H.: An iterated local search heuristic for multi-capacity bin packing and machine reassignment problems. Expert Syst. Appl. 40(13), 5266–5275 (2013)
Mehta, D., O’Sullivan, B., Simonis, H.: Comparing solution methods for the machine reassignment problem. In: Milano, M. (ed.) CP 2012. LNCS, vol. 7514, pp. 782–797. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33558-7_56
Ritt, M.R.P.: An algorithmic study of the machine reassignment problem. Ph.D. thesis, Universidade Federal do Rio Grande do Sul (2012)
Sabar, N.R., Song, A.: Grammatical evolution enhancing simulated annealing for the load balancing problem in cloud computing. In: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference, pp. 997–1003. ACM (2016)
Sabar, N.R., Song, A., Zhang, M.: A variable local search based memetic algorithm for the load balancing problem in cloud computing. In: Squillero, G., Burelli, P. (eds.) EvoApplications 2016. LNCS, vol. 9597, pp. 267–282. Springer, Cham (2016). doi:10.1007/978-3-319-31204-0_18
Turabieh, H., Abdullah, S., McCollum, B.: Electromagnetism-like mechanism with force decay rate great deluge for the course timetabling problem. In: Wen, P., Li, Y., Polkowski, L., Yao, Y., Tsumoto, S., Wang, G. (eds.) RSKT 2009. LNCS, vol. 5589, pp. 497–504. Springer, Heidelberg (2009). doi:10.1007/978-3-642-02962-2_63
Turky, A., Sabar, N.R., Sattar, A., Song, A.: Parallel late acceptance hill-climbing algorithm for the Google machine reassignment problem. In: Kang, B.H., Bai, Q. (eds.) AI 2016. LNCS (LNAI), vol. 9992, pp. 163–174. Springer, Cham (2016). doi:10.1007/978-3-319-50127-7_13
Turky, A., Sabar, N.R., Song, A.: An evolutionary simulating annealing algorithm for Google machine reassignment problem. In: Leu, G., Singh, H.K., Elsayed, S. (eds.) Intelligent and Evolutionary Systems. PALO, vol. 8, pp. 431–442. Springer, Cham (2017). doi:10.1007/978-3-319-49049-6_31
Turky, A., Sabar, N.R., Song, A.: Cooperative evolutionary heterogeneous simulated annealing algorithm for Google machine reassignment problem. Genetic Program. Evolvable Mach. 1–28 (2017). doi:10.1007/s10710-017-9305-0
Turky, A., Sabar, N.R., Song, A.: Neighbourhood analysis: a case study on Google machine reassignment problem. In: Wagner, M., Li, X., Hendtlass, T. (eds.) ACALCI 2017. LNCS, vol. 10142, pp. 228–237. Springer, Cham (2017). doi:10.1007/978-3-319-51691-2_20
Wang, Z., Lü, Z., Ye, T.: Multi-neighborhood local search optimization for machine reassignment problem. Comput. Oper. Res. 68, 16–29 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Turky, A., Sabar, N.R., Sattar, A., Song, A. (2017). Multi-neighbourhood Great Deluge for Google Machine Reassignment Problem. In: Shi, Y., et al. Simulated Evolution and Learning. SEAL 2017. Lecture Notes in Computer Science(), vol 10593. Springer, Cham. https://doi.org/10.1007/978-3-319-68759-9_57
Download citation
DOI: https://doi.org/10.1007/978-3-319-68759-9_57
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-68758-2
Online ISBN: 978-3-319-68759-9
eBook Packages: Computer ScienceComputer Science (R0)