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

Geoffrey Pruvost; Bilel Derbel; Arnaud Liefooghe; Ke Li; Qingfu Zhang

doi:10.1007/978-3-030-43680-3_9

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arXiv · 2004.06961

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

Geoffrey Pruvost·Bilel Derbel·Arnaud Liefooghe·Ke Li·Qingfu Zhang

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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.

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Geoffrey Pruvost, Bilel Derbel, Arnaud Liefooghe, Ke Li, Qingfu Zhang. 2020-04-15. On the Combined Impact of Population Size and Sub-problem Selection in MOEA/D. https://doi.org/10.1007/978-3-030-43680-3_9

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