Duro, João A and Kumar Saxena, Dhish and Deb, Kalyanmoy and Zhang, Qingfu (2014) Machine learning based decision support for many-objective optimization problems. Neurocomputing, 146. pp. 30-47. DOI https://doi.org/10.1016/j.neucom.2014.06.076
Duro, João A and Kumar Saxena, Dhish and Deb, Kalyanmoy and Zhang, Qingfu (2014) Machine learning based decision support for many-objective optimization problems. Neurocomputing, 146. pp. 30-47. DOI https://doi.org/10.1016/j.neucom.2014.06.076
Duro, João A and Kumar Saxena, Dhish and Deb, Kalyanmoy and Zhang, Qingfu (2014) Machine learning based decision support for many-objective optimization problems. Neurocomputing, 146. pp. 30-47. DOI https://doi.org/10.1016/j.neucom.2014.06.076
Abstract
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-à-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. © 2014 Elsevier B.V.
Item Type: | Article |
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Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 08 Jul 2015 14:15 |
Last Modified: | 05 Dec 2024 16:42 |
URI: | http://repository.essex.ac.uk/id/eprint/14101 |