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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 useLs 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 theMoea/d framework. By extensive experiments on a new set of bi-objective traveling salesman problems with tunable correlated objectives, we analyze these policies with differentMoea/d parameters. Our empirical study has shed some insights about the impact of theLs move strategy on the anytime performance of the algorithm.
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Authors and Affiliations
University Lille, CNRS, UMR 9189 – CRIStAL/Inria Lille-Nord Europe, Villeneuve-d’ascq, France
Bilel Derbel & Arnaud Liefooghe
Computer Science Department, City University, Kowloon Tong, Hong Kong
Qingfu Zhang
Faculty of Engineering, Shinshu University, Nagano, Japan
Hernan Aguirre & Kiyoshi Tanaka
- Bilel Derbel
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- Arnaud Liefooghe
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- Qingfu Zhang
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- Hernan Aguirre
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- Kiyoshi Tanaka
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Editors and Affiliations
University of Manchester, Manchester, United Kingdom
Julia Handl
Edinburgh Napier University, Edinburgh, United Kingdom
Emma Hart
Aston University, Birmingham, United Kingdom
Peter R. Lewis
University of Manchester, Manchester, United Kingdom
Manuel López-Ibáñez
University of Stirling, Stirling, United Kingdom
Gabriela Ochoa
Edinburgh Napier University, Edinburgh, United Kingdom
Ben Paechter
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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
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