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Machine learning based decision support for many-objective optimization problems
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DOI：10.1016/j.neucom.2014.06.076.png)
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摘要
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Multiple Criteria Decision-Making (MCDM) based Multi-objective Evolutionary Algorithms (MOEAs) are increasingly becoming popular for dealing with optimization problems with more than three objectives, commonly termed as many-objective optimization problems (MaOPs). These algorithms elicit preferences from a single or multiple Decision Makers (DMs), a priori or interactively, to guide the search towards the solutions most preferred by the DM(s), as against the whole Pareto-optimal Front (POF). Despite its promise for dealing with MaOPs, the utility of this approach is impaired by the lack of-objectivity; repeatability; consistency; and coherence in DM's preferences. This paper proposes a machine learning based framework to counter the above limitations. Towards it, the preference-structure of the different objectives embedded in the problem model is learnt in terms of: a smallest set of conflicting objectives which can generate the same POF as the original problem; the smallest objective sets corresponding to pre-specified errors; and the objective sets of pre-specified sizes that correspond to minimum error. While the focus is on demonstrating how the proposed framework could serve as a decision support for the DM, its performance is also studied vis-a-vis an alternative approach (based on dominance relation preservation), for a wide range of test problems and a real-world problem. The results mark a new direction for MCDM based MOEAs for MaOPs. (C) 2014 Elsevier B.V. All rights reserved.
Keyword:
Evolutionary many-objective optimization
Principal component analysis
Maximum variance unfolding
Kernels and multiple criteria decision-making
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IF:
6.5
论文数:
2.5W
被引数:
6.5W
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