Abstract
Pareto local search (PLS) is an extension of iterative improvement methods for multi-objective combinatorial optimization problems and an important part of several state-of-the-art multi-objective optimizers. PLS stops when all neighbors of the solutions in its solution archive are dominated. If terminated before completion, it may produce a poor approximation to the Pareto front. This paper proposes variants of PLS that improve its anytime behavior, that is, they aim to maximize the quality of the Pareto front at each time step. Experimental results on the bi-objective traveling salesman problem show a large improvement of the proposed anytime PLS algorithm over the classical one.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Beume, N., Naujoks, B., Emmerich, M.: SMS-EMOA: Multiobjective selection based on dominated hypervolume. European Journal of Operational Research 181(3), 1653–1669 (2007)
Conover, W.J.: Practical Nonparametric Statistics. John Wiley & Sons (1999)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 181–197 (2002)
Dubois-Lacoste, J., López-Ibáñez, M., Stützle, T.: A hybrid TP+PLS algorithm for bi-objective flow-shop scheduling problems. Computers & Operations Research 38(8), 1219–1236 (2011)
Dubois-Lacoste, J., López-Ibáñez, M., Stützle, T.: Improving the anytime behavior of two-phase local search. Annals of Mathematics and Artificial Intelligence 61(2), 125–154 (2011)
Dubois-Lacoste, J., López-Ibáñez, M., Stützle, T.: Supplementary Material: Pareto Local Search Variants for Anytime Bi-Objective Optimization (2012), http://iridia.ulb.ac.be/supp/IridiaSupp2012-004
Ehrgott, M., Gandibleux, X.: Approximative solution methods for combinatorial multicriteria optimization. TOP 12(1), 1–88 (2004)
Liefooghe, A., Mesmoudi, S., Humeau, J., Jourdan, L., Talbi, E.G.: On dominance-based multiobjective local search: design, implementation and experimental analysis on scheduling and traveling salesman problems. Journal of Heuristics (2011)
Loudni, S., Boizumault, P.: Combining VNS with constraint programming for solving anytime optimization problems. European Journal of Operational Research 191, 705–735 (2008)
Lust, T., Jaszkiewicz, A.: Speed-up techniques for solving large-scale biobjective TSP. Computers & Operations Research 37(3), 521–533 (2010)
Lust, T., Teghem, J.: The multiobjective multidimensional knapsack problem: a survey and a new approach. Arxiv preprint arXiv:1007.4063 (2010)
Lust, T., Teghem, J.: Two-phase Pareto local search for the biobjective traveling salesman problem. Journal of Heuristics 16(3), 475–510 (2010)
Paquete, L., Chiarandini, M., Stützle, T.: Pareto local optimum sets in the biobjective traveling salesman problem: An experimental study. In: Gandibleux, X., et al. (eds.) Metaheuristics for Multiobjective Optimisation. LNEMS, vol. 535, pp. 177–200. Springer (2004)
Paquete, L., Stützle, T.: Design and analysis of stochastic local search for the multiobjective traveling salesman problem. Computers & Operations Research 36(9), 2619–2631 (2009)
Zilberstein, S.: Using anytime algorithms in intelligent systems. AI Magazine 17(3), 73–83 (1996)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength Pareto evolutionary algorithm for multiobjective optimization. In: Giannakoglou, K., et al. (eds.) Evolutionary Methods for Design, Optimisation and Control, pp. 95–100. CIMNE, Barcelona (2002)
Zitzler, E., Thiele, L.: Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN V 1998. LNCS, vol. 1498, pp. 292–301. Springer, Heidelberg (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dubois-Lacoste, J., López-Ibáñez, M., Stützle, T. (2012). Pareto Local Search Algorithms for Anytime Bi-objective Optimization. In: Hao, JK., Middendorf, M. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2012. Lecture Notes in Computer Science, vol 7245. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29124-1_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-29124-1_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29123-4
Online ISBN: 978-3-642-29124-1
eBook Packages: Computer ScienceComputer Science (R0)