Abstract
It is generally believed that Local search (Ls) should be used as a basic tool in multi-objective evolutionary computation for combinatorial optimization. However, not much effort has been made to investigate how to efficiently use Ls in multi-objective evolutionary computation algorithms. In this paper, we study some issues in the use of cooperative scalarizing local search approaches for decomposition-based multi-objective combinatorial optimization. We propose and study multiple move strategies in the Moea/d framework. By extensive experiments on a new set of bi-objective traveling salesman problems with tunable correlated objectives, we analyze these policies with different Moea/d parameters. Our empirical study has shed some insights about the impact of the Ls move strategy on the anytime performance of the algorithm.
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
Similar content being viewed by others
References
Chang, P.C., Chen, S.H., Zhang, Q., Lin, J.L.: MOEA/D for flowshop scheduling problems. In: CEC, pp. 1433–1438 (2008)
Hoos, H., Stützle, T.: Stochastic Local Search: Foundations and Applications. Morgan Kaufmann, Burlington (2004)
Ishibuchi, H., Murata, T.: A multi-objective genetic local search algorithm and its application to flowshop scheduling. IEEE Trans. Cyber. 28(3), 392–403 (1998)
Jaszkiewicz, A.: Genetic local search for multi-objective combinatorial optimization. EJOR 137(1), 50–71 (2002)
Ke, L., Zhang, Q., Battiti, R.: MOEA/D-ACO: a multiobjective evolutionary algorithm using decomposition and ant colony. IEEE Trans. Cyber. 43(6), 1845–1859 (2013)
Ke, L., Zhang, Q., Battiti, R.: Hybridization of decomposition and local search for multiobjective optimization. IEEE Trans. Cyber. 44(10), 1808–1820 (2014)
Knowles, J., Thiele, L., Zitzler, E.: A tutorial on the performance assessment of stochastic multiobjective optimizers. TIK report 214, Zurich, Switzerland (2006)
Li, H., Zhang, Q.: Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II. IEEE TEC 13(2), 284–302 (2009)
Liefooghe, A., Mesmoudi, S., Humeau, J., Jourdan, L., Talbi, E.G.: On dominance-based local search. J. Heuristics 18(2), 317–352 (2012)
Lust, T., Teghem, J.: Two-phase Pareto local search for the biobjective traveling salesman problem. J. Heuristics 16(3), 475–510 (2010)
Palacios Alonso, J.J., Derbel, B.: On maintaining diversity in MOEA/D: application to a biobjective combinatorial FJSP. In: GECCO, pp. 719–726 (2015)
Paquete, L., Stützle, T.: Design and analysis of stochastic local search for the multiobjective traveling salesman problem. COR 36(9), 2619–2631 (2009)
Verel, S., Liefooghe, A., Jourdan, L., Dhaenens, C.: On the structure of multi-objective combinatorial search space: MNK-landscapes with correlated objectives. Eur. J. Oper. Res. 227(2), 331–342 (2013)
Zhang, Q., Li, H.: MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE TEC 11(6), 712–731 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Derbel, B., Liefooghe, A., Zhang, Q., Aguirre, H., Tanaka, K. (2016). Multi-objective Local Search Based on Decomposition. In: Handl, J., Hart, E., Lewis, P., López-Ibáñez, M., Ochoa, G., Paechter, B. (eds) Parallel Problem Solving from Nature – PPSN XIV. PPSN 2016. Lecture Notes in Computer Science(), vol 9921. Springer, Cham. https://doi.org/10.1007/978-3-319-45823-6_40
Download citation
DOI: https://doi.org/10.1007/978-3-319-45823-6_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-45822-9
Online ISBN: 978-3-319-45823-6
eBook Packages: Computer ScienceComputer Science (R0)