On the Combined Impact of Population Size and Sub-problem Selection in MOEA/D
This work provides incremental insights for researchers in evolutionary computation, focusing on parameter tuning in multi-objective optimization.
The paper investigates how population size and sub-problem selection strategies affect the performance of decomposition-based multi-objective evolutionary algorithms, finding that a simple random strategy outperforms existing sophisticated ones on combinatorial NK landscapes.
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.