Name
Abstract | ||
|---|---|---|
User preference integration is of great importance in multiobjective optimization, in particular in many objective optimization. Preferences have long been considered in traditional multicriteria decision making (MCDM) which is based on mathematical programming and recently it is integrated in evolutionary multiobjective optimization (EMO), resulting in focus on preferred parts of the Pareto front instead of the whole Pareto front. The number of publications and results on preference-based multiobjective evolutionary algorithms (PMOEAs) has increased rapidly over the past decade. There already exists a large variety of preference handling methods and EMO methods, which have been combined in various ways. This article proposes to use the Web Ontology Language (OWL) to model and systematize the results developed in this field. An extensive review of the existing work is provided, based on which an ontology is built and instantiated with state of the art results. The OWL ontology is made public and open to future extension. Moreover, the usage of the ontology is exemplified for different use-cases, including training new researchers into this knowledge domain, querying for methods that match an application problem in engineering optimization, checking existence of combinations of preference models and EMO techniques, and discovering opportunities for new research and open research questions. |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Neural and Evolutionary Computing | Open research,Ontology,Multiple-criteria decision analysis,Evolutionary algorithm,Computer science,Multi-objective optimization,Artificial intelligence,Engineering optimization,Machine learning,Ontology language,Web Ontology Language |
DocType | Volume | Citations |
Journal | abs/1609.08082 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Longmei Li | 1 | 0 | 0.68 |
| Iryna Yevseyeva | 2 | 72 | 14.98 |
| Vítor Basto Fernandes | 3 | 21 | 5.60 |
| Heike Trautmann | 4 | 623 | 43.22 |
| Ning Jing | 5 | 7 | 1.50 |
| Michael T. M. Emmerich | 6 | 247 | 22.74 |