Interactive evolutionary algorithms with decision-maker's preferences for solving interval multi-objective optimization problems
Interval multi-objective optimization problems (IMOPs), whose parameters are intervals, are considerably ubiquitous in real-world applications. Previous evolutionary algorithms (EAs) aim at finding the well-converged and evenly-distributed Pareto front. An EA incorporating with a decision-maker (DM)...
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| Veröffentlicht in: | Neurocomputing (Amsterdam) Jg. 137; S. 241 - 251 |
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| Hauptverfasser: | , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
05.08.2014
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| Schlagworte: | |
| ISSN: | 0925-2312 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | Interval multi-objective optimization problems (IMOPs), whose parameters are intervals, are considerably ubiquitous in real-world applications. Previous evolutionary algorithms (EAs) aim at finding the well-converged and evenly-distributed Pareto front. An EA incorporating with a decision-maker (DM)'s preferences was presented in this study to obtain a Pareto-optimal subset that meets the DM's preferences. In this algorithm, the DM's preferences in terms of the relative importance of objectives were interactively input, and the corresponding preferred regions were then obtained. Based on these regions, solutions with the same rank were further distinguished to guide the search towards the DM's preferred region. The proposed method was empirically evaluated on four IMOPs and compared with other state-of-the-art methods. The experimental results demonstrated the simplicity and the effectiveness of the proposed method. |
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| Bibliographie: | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 |
| ISSN: | 0925-2312 |
| DOI: | 10.1016/j.neucom.2013.04.052 |