Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
UBQP instances have been obtained with the instance generator provided at http://www.personalas.ktu.lt/~ginpalu/ubqop_its.html.
- 2.
We use the term expected global optimum when a same fitness is always reached by a set of methods. Constantly obtaining the same final solution (or fitness) does not guarantee its optimality, which could only be proved using complete methods.
References
Sörensen, K.: Metaheuristics—the metaphor exposed. Int. Trans. Oper. Res. 22(1), 3–18 (2015)
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)
Wright, S.: The roles of mutation, inbreeding, crossbreeding, and selection in evolution. vol. 1 (1932)
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)
Basseur, M., Goëffon, A.: Climbing combinatorial fitness landscapes. Appl. Soft Comput. 30, 688–704 (2015)
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)
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)
Gary, M.R., Johnson, D.S.: Computers and intractability: a guide to the theory of NP-completeness (1979)
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)
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). https://doi.org/10.1007/978-3-642-15844-5_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)
Loureno, H.R., Martin, O.C., Stutzle, T.: Iterated local search. Int. Ser. Oper. Res. Manag. Sci. 321–354 (2003)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Tari, S., Basseur, M., Goëffon, A. (2018). Worst Improvement Based Iterated Local Search. In: Liefooghe, A., López-Ibáñez, M. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2018. Lecture Notes in Computer Science(), vol 10782. Springer, Cham. https://doi.org/10.1007/978-3-319-77449-7_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-77449-7_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-77448-0
Online ISBN: 978-3-319-77449-7
eBook Packages: Computer ScienceComputer Science (R0)