Evolutionary Learning Based Iterated Local Search for Google Machine Reassignment Problems

  • Ayad TurkyEmail author
  • Nasser R. Sabar
  • Abdul Sattar
  • Andy Song
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10593)


Iterated Local Search (ILS) is a simple yet powerful optimisation method that iteratively invokes a local search procedure with renewed starting points by perturbation. Due to the complexity of search landscape, different ILS strategies may better suit different problem instances or different search stages. To address this issue, this work proposes a new ILS framework which selects the most suited components of ILS based on evolutionary meta-learning. It has three additional components other than ILS: meta-feature extraction, meta-learning and classification. The meta-feature and meta-learning steps are to generate a multi-class classifier by training on a set of existing problem instances. The generated classifier then selects the most suitable ILS setting when performing on new instances. The classifier is generated by Genetic Programming. The effectiveness of the proposed ILS framework is demonstrated on the Google Machine Reassignment Problem. Experimental results show that the proposed framework is highly competitive compared to 10 state-of-the-art methods reported in the literature.


Iterated Local Search Meta-learning Google machine reassignment problem Genetic programming 


  1. 1.
    Roadef/euro challenge 2012: Machine reassignment.
  2. 2.
    Afsar, H.M., Artigues, C., Bourreau, E., Kedad-Sidhoum, S.: Machine reassignment problem: the ROADEF/EURO challenge 2012. Ann. Oper. Res. 242(1), 1–17 (2016)Google Scholar
  3. 3.
    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)CrossRefGoogle Scholar
  4. 4.
    Brandt, F., Speck, J., Völker, M.: Constraint-based large neighborhood search for machine reassignment. Ann. Oper. Res. 242(1), 63–91 (2016)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    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.: Practice Experience 41(1), 23–50 (2011)Google Scholar
  6. 6.
    de Carvalho, A.C.P.L.F., Freitas, A.A.: A tutorial on multi-label classification techniques. In: Abraham, A., Hassanien, AE., Snáŝel, V. (eds.) Foundations of Computational Intelligence, Studies in Computational Intelligence, vol. 5, pp. 177–195. Springer, Heidelberg (2009). doi: 10.1007/978-3-642-01536-6_8
  7. 7.
    García, S., Fernández, A., Luengo, J., Herrera, F.: Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental analysis of power. Inf. Sci. 180(10), 2044–2064 (2010)CrossRefGoogle Scholar
  8. 8.
    Gavranović, H., Buljubašić, M., Demirović, E.: Variable neighborhood search for Google machine reassignment problem. Electron. Notes Discrete Math. 39, 209–216 (2012)CrossRefzbMATHGoogle Scholar
  9. 9.
    Lopes, R., Morais, V.W.C., Noronha, T.F., Souza, V.A.A.: Heuristics and matheuristics for a real-life machine reassignment problem. Int. Trans. Oper. Res. 22(1), 77–95 (2015)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Lourenço, H.R., Martin, O., Stützle, T.: A beginners introduction to iterated local search. In: Proceedings of MIC, pp. 1–6 (2001)Google Scholar
  11. 11.
    Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated local search. In: Glover, F., Kochenberger, G.A. (eds.) Handbook of Metaheuristics. International Series in Operations Research and Management Science, vol. 57, pp. 320–353. Springer, Heidelberg (2003). doi: 10.1007/0-306-48056-5_11
  12. 12.
    Lourenço, H.R., Martin, O.C., Stützle, T.: Iterated local search: framework and applications. In: Gendreau, M., Potvin, J.Y. (eds.) Handbook of Metaheuristics. International Series in Operations Research and Management Science, vol. 146, pp. 363–397. Springer, Heidelberg (2010). doi: 10.1007/978-1-4419-1665-5_12
  13. 13.
    Malitsky, Y., Mehta, D., O’Sullivan, B., Simonis, H.: Tuning parameters of large neighborhood search for the machine reassignment problem. In: Gomes, C., Sellmann, M. (eds.) CPAIOR 2013. LNCS, vol. 7874, pp. 176–192. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-38171-3_12 CrossRefGoogle Scholar
  14. 14.
    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)CrossRefGoogle Scholar
  15. 15.
    Mehta, D., O’Sullivan, B., Simonis, H.: Comparing solution methods for the machine reassignment problem. In: Milano, M. (ed.) CP 2012. LNCS, pp. 782–797. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-33558-7_56 CrossRefGoogle Scholar
  16. 16.
    Portal, G.M., Ritt, M., Borba, L.M., Buriol, L.S.: Simulated annealing for the machine reassignment problem. Ann. Oper. Res. 242(1), 93–114 (2016)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Ritt, M.R.P.: An algorithmic study of the machine reassignment problem. Ph.D. thesis, Universidade Federal do Rio Grande do Sul (2012)Google Scholar
  18. 18.
    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)Google Scholar
  19. 19.
    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 CrossRefGoogle Scholar
  20. 20.
    Turky, A., Moser, I., Aleti, A.: An iterated local search with guided perturbation for the heterogeneous fleet vehicle routing problem with time windows and three-dimensional loading constraints. In: Wagner, M., Li, X., Hendtlass, T. (eds.) ACALCI 2017. LNCS, vol. 10142, pp. 279–290. Springer, Cham (2017). doi: 10.1007/978-3-319-51691-2_24 CrossRefGoogle Scholar
  21. 21.
    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, vol. 9992, pp. 163–174. Springer, Cham (2016). doi: 10.1007/978-3-319-50127-7_13 CrossRefGoogle Scholar
  22. 22.
    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 CrossRefGoogle Scholar
  23. 23.
    Turky, A., Sabar, N.R., Song, A.: Cooperative evolutionary heterogeneous simulated annealing algorithm for Google machine reassignment problem. In: Genetic Programming and Evolvable Machines, pp. 1–28 (2017). doi: 10.1007/s10710-017-9305-0
  24. 24.
    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 CrossRefGoogle Scholar
  25. 25.
    Wang, Z., Lü, Z., Ye, T.: Multi-neighborhood local search optimization for machine reassignment problem. Comput. Oper. Res. 68, 16–29 (2016)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Ayad Turky
    • 1
    Email author
  • Nasser R. Sabar
    • 2
  • Abdul Sattar
    • 3
  • Andy Song
    • 1
  1. 1.School of Computer Science and I.T.RMIT UniversityMelbourneAustralia
  2. 2.Queensland University of TechnologyBrisbaneAustralia
  3. 3.Griffith UniversityNathanAustralia

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