Hybrid evolutionary multi-objective optimisation using outranking-based ordinal classification methods
A large number of real-world problems require optimising several objective functions at the same time, which are generally in conflict. Many of these problems have been addressed through multi-objective evolutionary algorithms. In this paper, we propose a new hybrid evolutionary algorithm whose ma...
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| Other Authors: | , , , , |
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| Format: | Artículo |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://doi.org/10.1016/j.swevo.2020.100652 https://www.sciencedirect.com/science/article/abs/pii/S2210650219304274 |
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| Summary: | A large number of real-world problems require optimising several objective functions at the same time, which are
generally in conflict. Many of these problems have been addressed through multi-objective evolutionary algorithms.
In this paper, we propose a new hybrid evolutionary algorithm whose main feature is the incorporation of
the Decision Maker’s (DM’s) preferences through multi-criteria ordinal classification methods in early stages of
the optimisation process, being progressively updated. This increases the selective pressure towards the privileged
zone of the Pareto front more in agreement with the DM’s preferences. An extensive experimental research was
conducted to answer three main questions: i) to what extent the proposal improves the convergence towards the
region of interest for the DM; ii) to what extent the proposal becomes more relevant as the number of objectives
increases, and iii) to what extent the effectiveness of the hybrid algorithm depends on the particular multi-criteria
method used to assign solutions to ordered classes. The issues used to evaluate our proposal and answer the
questions were seven scalable test problems from the DTLZ test suite and some instances of project portfolio
optimisation problems, with three and eight objectives. Compared to MOEA/D and MOEA/D-DE, the results
showed that the proposed strategy obtains a better convergence towards the region of interest for the DM and also
performs better characterisation of that zone on a wide range of objective functions. |
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