Worst Improvement Based Iterated Local Search

  • Sara Tari
  • Matthieu Basseur
  • Adrien Goëffon
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10782)


To solve combinatorial optimization problems, many metaheuristics use first or best improvement hill-climbing as intensification mechanism in order to find local optima. In particular, first improvement offers a good tradeoff between computation cost and quality of reached local optima. In this paper, we investigate a worst improvement-based moving strategy, never considered in the literature. Such a strategy is able to reach good local optima despite requiring a significant additional computation cost. Here, we investigate if such a pivoting rule can be efficient when considered within metaheuristics, and especially within iterated local search (ILS). In our experiments, we compare an ILS using a first improvement pivoting rule to an ILS using an approximated version of worst improvement pivoting rule. Both methods are launched with the same number of evaluations on bit-string based fitness landscapes. Results are analyzed using some landscapes’ features in order to determine if the worst improvement principle should be considered as a moving strategy in some cases.


  1. 1.
    Sörensen, K.: Metaheuristics—the metaphor exposed. Int. Trans. Oper. Res. 22(1), 3–18 (2015)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Whitley, D., Howe, A.E., Hains, D.: Greedy or not? Best improving versus first improving stochastic local search for MAXSAT. In: AAAI Conference on Artificial Intelligence (2013)Google Scholar
  3. 3.
    Wright, S.: The roles of mutation, inbreeding, crossbreeding, and selection in evolution. vol. 1 (1932)Google Scholar
  4. 4.
    Ochoa, G., Tomassini, M., Verel, S., Darabos, C.: A study of NK landscapes’ basins and local optima networks. In: Conference on Genetic and Evolutionary Computation, pp. 555–562. ACM (2008)Google Scholar
  5. 5.
    Basseur, M., Goëffon, A.: Climbing combinatorial fitness landscapes. Appl. Soft Comput. 30, 688–704 (2015)CrossRefGoogle Scholar
  6. 6.
    Malan, K.M., Engelbrecht, A.P.: A survey of techniques for characterising fitness landscapes and some possible ways forward. Inf. Sci. 241, 148–163 (2013)CrossRefGoogle Scholar
  7. 7.
    Kauffman, S.A., Weinberger, E.D.: The NK model of rugged fitness landscapes and its application to maturation of the immune response. J. Theoret. Biol. 141(2), 211–245 (1989)CrossRefGoogle Scholar
  8. 8.
    Gary, M.R., Johnson, D.S.: Computers and intractability: a guide to the theory of NP-completeness (1979)Google Scholar
  9. 9.
    Basseur, M., Goëffon, A., Lardeux, F., Saubion, F., Vigneron, V.: On the attainability of NK landscapes global optima. In: 7th Annual Symposium on Combinatorial Search (2014)Google Scholar
  10. 10.
    Ochoa, G., Verel, S., Tomassini, M.: First-improvement vs. best-improvement local optima networks of NK landscapes. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN 2010. LNCS, vol. 6238, pp. 104–113. Springer, Heidelberg (2010). Scholar
  11. 11.
    Basseur, M., Goëffon, A.: Hill-climbing strategies on various landscapes: an empirical comparison. In: Genetic and Evolutionary Computation Conference (GECCO), pp. 479–486. ACM (2013)Google Scholar
  12. 12.
    Loureno, H.R., Martin, O.C., Stutzle, T.: Iterated local search. Int. Ser. Oper. Res. Manag. Sci. 321–354 (2003)Google Scholar

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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Laboratoire d’Etude et de Recherche en Informatique d’AngersUFR SciencesAngers Cedex 01France

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