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On the Combined Impact of Population Size and Sub-problem Selection in MOEA/D. (arXiv:2004.06961v1 [cs.NE])

Date:

[Submitted on 15 Apr 2020]

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Abstract: 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.

Submission history

From: Geoffrey Pruvost [view email] [via CCSD proxy]
[v1]
Wed, 15 Apr 2020 09:13:32 UTC (5,017 KB)

Source: http://arxiv.org/abs/2004.06961

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