TY - GEN
T1 - Multi-objective local search based on decomposition
AU - Derbel, Bilel
AU - Liefooghe, Arnaud
AU - Zhang, Qingfu
AU - Aguirre, Hernan
AU - Tanaka, Kiyoshi
PY - 2016
Y1 - 2016
N2 - 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 multiobjective 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.
AB - 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 multiobjective 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.
UR - http://www.scopus.com/inward/record.url?scp=84988527836&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84988527836&origin=recordpage
U2 - 10.1007/978-3-319-45823-6_40
DO - 10.1007/978-3-319-45823-6_40
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9783319458229
VL - 9921 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 431
EP - 441
BT - Parallel Problem Solving from Nature
A2 - Hart, Emma
A2 - Paechter, Ben
A2 - Handl, Julia
A2 - López-Ibáñez, Manuel
A2 - Lewis, Peter R.
A2 - Ochoa, Gabriela
PB - Springer Verlag
T2 - 14th International Conference on Parallel Problem Solving from Nature, PPSN 2016
Y2 - 17 September 2016 through 21 September 2016
ER -