Ontology type: schema:ScholarlyArticle Open Access: True
2017-12
AUTHORSHanding Wang, Markus Olhofer, Yaochu Jin
ABSTRACTEvolutionary multi-objective optimization aims to provide a representative subset of the Pareto front to decision makers. In practice, however, decision makers are usually interested in only a particular part of the Pareto front of the multi-objective optimization problem. This is particularly true when the number of objectives becomes large. Over the past decade, preference-based multi-objective optimization has attracted increasing attention from both academia and industry due to its significance in both theory and practice. Significant progress has been made in evolutionary multi-objective optimization and multi-criteria decision communities, although many open issues still remain to be addressed. This paper provides a concise review on preference-based multi-objective optimization, including various preference modeling methods and existing preference-based optimization methods, as well as a brief discussion of the main future challenges. More... »
PAGES233-245
http://scigraph.springernature.com/pub.10.1007/s40747-017-0053-9
DOIhttp://dx.doi.org/10.1007/s40747-017-0053-9
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