Abstract
Combinatorial optimisation is often needed for solving real-world problems, which are often NP-hard so exact methods are not suitable. Instead local search methods are often effective to find near-optimal solutions quickly. However, it is difficult to determine which local search with what parameter setting should be optimal for a given problem. In this study two complex combinatorial optimisation are used, Multi-capacity Bin Packing Problems (MCBPP) and Google Machine Reassignment Problem (GMRP). Our experiments show that no single local search method could consistently achieve the best. They are sensitive to problem search space and parameters. Therefore we propose a hyper heuristic based method, which automatically selects the most appropriate local search during the search and tune the parameters accordingly. The results show that our proposed hyper-heuristic approach is effective and can achieve the overall best on multiple instances of both MCBPP and GMRP.
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
Burke, E.K., Bykov, Y.: A late acceptance strategy in hill-climbing for exam timetabling problems. In: PATAT 2008 Conference, Montreal, Canada (2008)
Caprara, A., Toth, P.: Lower bounds and algorithms for the 2-dimensional vector packing problem. Discret. Appl. Math. 111(3), 231–262 (2001)
Dueck, G.: New optimization heuristics: the great deluge algorithm and the record-to-record travel. J. Comput. Phys. 104(1), 86–92 (1993)
Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P., et al.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)
Lourenço, H.R., Martin, O., Stützle, T.: A beginners introduction to iterated local search. In: Proceedings of MIC, pp. 1–6 (2001)
Monaci, M., Toth, P.: A set-covering-based heuristic approach for bin-packing problems. INFORMS J. Comput. 18(1), 71–85 (2006)
ROADEF: ROADEF/EURO challenge 2012: machine reassignment. http://challenge.roadef.org/2012/en/
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). https://doi.org/10.1007/978-3-319-31204-0_18
Spieksma, F.C.R.: A branch-and-bound algorithm for the two-dimensional vector packing problem. Comput. Oper. Res. 21(1), 19–25 (1994)
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). https://doi.org/10.1007/978-3-319-50127-7_13
Turky, A., Sabar, N.R., Sattar, A., Song, A.: Evolutionary learning based iterated local search for Google machine reassignment problems. In: Shi, Y., et al. (eds.) SEAL 2017. LNCS, vol. 10593, pp. 409–421. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68759-9_34
Turky, A., Sabar, N.R., Sattar, A., Song, A.: Multi-neighbourhood Great Deluge for Google machine reassignment problem. In: Shi, Y., et al. (eds.) SEAL 2017. LNCS, vol. 10593, pp. 706–715. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68759-9_57
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). https://doi.org/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. Genet. Program. Evolvable Mach. 19(1–2), 183–210 (2018)
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 (LNAI), vol. 10142, pp. 228–237. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-51691-2_20
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Turky, A., Sabar, N.R., Dunstall, S., Song, A. (2018). Hyper-heuristic Based Local Search for Combinatorial Optimisation Problems. In: Mitrovic, T., Xue, B., Li, X. (eds) AI 2018: Advances in Artificial Intelligence. AI 2018. Lecture Notes in Computer Science(), vol 11320. Springer, Cham. https://doi.org/10.1007/978-3-030-03991-2_30
Download citation
DOI: https://doi.org/10.1007/978-3-030-03991-2_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-03990-5
Online ISBN: 978-3-030-03991-2
eBook Packages: Computer ScienceComputer Science (R0)