This paper intends to understand and to improve the working principle of
decomposition-based multi-objective evolutionary algorithms. We review the
design of the well-established Moea/d framework to support the smooth
integration of different strategies for sub-problem selection, while
emphasizing the role of the population size and of the number of offspring
created at each generation. By conducting a comprehensive empirical analysis on
a wide range of multi-and many-objective combinatorial NK landscapes, we
provide new insights into the combined effect of those parameters on the
anytime performance of the underlying search process. In particular, we show
that even a simple random strategy selecting sub-problems at random outperforms
existing sophisticated strategies. We also study the sensitivity of such
strategies with respect to the ruggedness and the objective space dimension of
the target problem.Comment: European Conference on Evolutionary Computation in Combinatorial
Optimization, Apr 2020, Seville, Spai
