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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This work was supported by the French national research agency (ANR-16-CE23-0013-01) and the Research Grants Council of Hong Kong (RGC Project No. A-CityU101/16).
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Pruvost, G., Derbel, B., Liefooghe, A., Li, K., Zhang, Q. (2020). On the Combined Impact of Population Size and Sub-problem Selection in MOEA/D. In: Paquete, L., Zarges, C. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2020. Lecture Notes in Computer Science(), vol 12102. Springer, Cham. https://doi.org/10.1007/978-3-030-43680-3_9
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