Diversified Late Acceptance Search

  • Majid Namazi
  • Conrad Sanderson
  • M. A. Hakim Newton
  • Md Masbaul Alam Polash
  • Abdul Sattar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11320)


The well-known Late Acceptance Hill Climbing (LAHC) search aims to overcome the main downside of traditional Hill Climbing (HC) search, which is often quickly trapped in a local optimum due to strictly accepting only non-worsening moves within each iteration. In contrast, LAHC also accepts worsening moves, by keeping a circular array of fitness values of previously visited solutions and comparing the fitness values of candidate solutions against the least recent element in the array. While this straightforward strategy has proven effective, there are nevertheless situations where LAHC can unfortunately behave in a similar manner to HC. For example, when a new local optimum is found, often the same fitness value is stored many times in the array. To address this shortcoming, we propose new acceptance and replacement strategies to take into account worsening, improving, and sideways movement scenarios with the aim to improve the diversity of values in the array. Compared to LAHC, the proposed Diversified Late Acceptance Search approach is shown to lead to better quality solutions that are obtained with a lower number of iterations on benchmark Travelling Salesman Problems and Quadratic Assignment Problems.


Local search Late Acceptance Diversification 


  1. 1.
    Abuhamdah, A.: Experimental result of late acceptance randomized descent algorithm for solving course timetabling problems. Int. J. Comput. Sci. Netw. Secur. 10(1), 192–200 (2010)Google Scholar
  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)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Appleby, J., Blake, D., Newman, E.: Techniques for producing school timetables on a computer and their application to other scheduling problems. Comput. J. 3(4), 237–245 (1961)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Bazargani, M., Lobo, F.G.: Parameter-less late acceptance hill-climbing. In: Genetic and Evolutionary Computation Conference, pp. 219–226 (2017)Google Scholar
  5. 5.
    Burke, E., Bykov, Y., Newall, J., Petrovic, S.: A time-predefined local search approach to exam timetabling problems. IIE Trans. 36(6), 509–528 (2004)CrossRefGoogle Scholar
  6. 6.
    Burke, E.K., Bykov, Y.: A late acceptance strategy in hill-climbing for examination timetabling problems. In: Conference on the Practice and Theory of Automated Timetabling (2008)Google Scholar
  7. 7.
    Burke, E.K., Bykov, Y.: The late acceptance hill-climbing heuristic. Eur. J. Oper. Res. 258(1), 70–78 (2017)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Bykov, Y., Petrovic, S.: A step counting hill climbing algorithm applied to university examination timetabling. J. Sched. 19(4), 479–492 (2016)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Curtin, R.R., Bhardwaj, S., Edel, M., Mentekidis, Y.: A generic and fast C++ optimization framework. arXiv 1711.06581 (2017)Google Scholar
  10. 10.
    Dueck, G.: New optimization heuristics: the great deluge algorithm and the record-to-record travel. J. Comput. Phys. 104(1), 86–92 (1993)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Dueck, G., Scheuer, T.: Threshold accepting: a general purpose optimization algorithm appearing superior to simulated annealing. J. Comput. Phys. 90(1), 161–175 (1990)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Fonseca, G.H., Santos, H.G., Carrano, E.G.: Late acceptance hill-climbing for high school timetabling. J. Sched. 19(4), 453–465 (2016)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Hoos, H.H., Stützle, T.: Stochastic Local Search: Foundations and Applications. Elsevier, Amsterdam (2004)zbMATHGoogle Scholar
  14. 14.
    Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Lin, S., Kernighan, B.W.: An effective heuristic algorithm for the traveling-salesman problem. Oper. Res. 21(2), 498–516 (1973)MathSciNetCrossRefGoogle Scholar
  16. 16.
    McMullan, P.: An extended implementation of the great deluge algorithm for course timetabling. In: Shi, Y., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2007. LNCS, vol. 4487, pp. 538–545. Springer, Heidelberg (2007). Scholar
  17. 17.
    Obit, J., Landa-Silva, D., Ouelhadj, D., Sevaux, M.: Non-linear great deluge with learning mechanism for solving the course timetabling problem. In: Metaheuristics International Conference (2009)Google Scholar
  18. 18.
    Smet, G.D., et al.: OptaPlanner User Guide. Red Hat and the community.
  19. 19.
    Wauters, T., Toffolo, T., Christiaens, J., Van Malderen, S.: The winning approach for the verolog solver challenge 2014: the swap-body vehicle routing problem. In: Belgian Conference on Operations Research (ORBEL) (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Majid Namazi
    • 1
    • 2
  • Conrad Sanderson
    • 2
    • 3
  • M. A. Hakim Newton
    • 1
  • Md Masbaul Alam Polash
    • 1
  • Abdul Sattar
    • 1
  1. 1.Griffith UniversityBrisbaneAustralia
  2. 2.Data61, CSIROSydneyAustralia
  3. 3.University of QueenslandBrisbaneAustralia

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