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

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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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Dates and versions

hal-02540291 , version 1 (14-04-2020)

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Geoffrey Pruvost, Bilel Derbel, Arnaud Liefooghe, Ke Li, Qingfu Zhang. On the Combined Impact of Population Size and Sub-problem Selection in MOEA/D. EvoCOP 2021 - 21st European Conference on Evolutionary Computation in Combinatorial Optimization, Apr 2020, Seville, Spain. ⟨10.1007/978-3-030-43680-3_9⟩. ⟨hal-02540291⟩
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