An Ontology of Preference-Based Multiobjective Metaheuristics
This work provides a systematization tool for researchers in multi-objective optimization, but it is incremental as it builds on existing review and ontology methods without introducing new optimization techniques.
The authors tackled the problem of organizing the growing literature on preference-based multi-objective metaheuristics by proposing an ontology using the Web Ontology Language (OWL) to model and systematize existing methods, making it publicly available for querying and analysis.
User preference integration is of great importance in multi-objective optimization, in particular in many objective optimization. Preferences have long been considered in traditional multicriteria decision making (MCDM) which is based on mathematical programming. Recently, it is integrated in multi-objective metaheuristics (MOMH), resulting in focus on preferred parts of the Pareto front instead of the whole Pareto front. The number of publications on preference-based multi-objective metaheuristics has increased rapidly over the past decades. There already exist various preference handling methods and MOMH methods, which have been combined in diverse ways. This article proposes to use the Web Ontology Language (OWL) to model and systematize the results developed in this field. A 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 querying for methods that match an engineering application, bibliometric analysis, checking existence of combinations of preference models and MOMH techniques, and discovering opportunities for new research and open research questions.