Abstract
In this paper, we build upon the previous efforts to enhance the search ability of Moea/d (a multi-objective decomposition-based algorithm), by investigating the idea of evolving the whole population simultaneously. We thereby propose new alternative selection and replacement strategies that can be combined in different ways within a generic and problem-independent framework. To assess the performance of our strategies, we conduct a comprehensive experimental study on bi-objective combinatorial optimization problems. More precisely, we consider ρMNK-landscapes and knapsack problems as a benchmark, and experiment a wide range of parameter configurations for Moea/d and its variants. Our analysis reveals the effectiveness of our strategies and their robustness to parameter settings. In particular, substantial improvements are obtained compared to the conventional Moea/d.
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Marquet, G., Derbel, B., Liefooghe, A., Talbi, EG. (2014). Shake Them All!. In: Bartz-Beielstein, T., Branke, J., Filipič, B., Smith, J. (eds) Parallel Problem Solving from Nature – PPSN XIII. PPSN 2014. Lecture Notes in Computer Science, vol 8672. Springer, Cham. https://doi.org/10.1007/978-3-319-10762-2_63
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DOI: https://doi.org/10.1007/978-3-319-10762-2_63
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10761-5
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