Section: New Results

Decomposition-based Multiobjective Optimization

Participants: Bilel Derbel, Arnaud Liefooghe (external collaborators: Hernan Aguirre and Kiyoshi Tanaka, Shinshu Univ., Japan; Qingfu Zhang, City Univ., Hong Kong)

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 [28], 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.