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This paper intends to understand and to improve the working principle ofdecomposition-based multi-objective evolutionary algorithms. We review thedesign of the well-established Moea/d framework to support the smoothintegration of different strategies for sub-problem selection, whileemphasizing the role of the population size and of the number of offspringcreated at each generation. By conducting a comprehensive empirical analysis ona wide range of multi-and many-objective combinatorial NK landscapes, weprovide new insights into the combined effect of those parameters on theanytime performance of the underlying search process. In particular, we showthat even a simple random strategy selecting sub-problems at random outperformsexisting sophisticated strategies. We also study the sensitivity of suchstrategies with respect to the ruggedness and the objective space dimension ofthe target problem.

On the Combined Impact of Population Size and Sub-problem Selection in MOEA/D