Browsing by Author "Li, Longmei"

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    Building and using an ontology of preference-based multiobjective evolutionary algorithms.
    (Springer, 2017-03-19) Li, Longmei; Yevseyeva, Iryna; Trautmann, H.; Jing, N.; Emmerich, M. T. M.
    Integrating user preferences in Evolutionary Multiobjective Optimization (EMO) is currently a prevalent research topic. There is a large variety of preference handling methods (originated from Multicriteria decision making, MCDM) and EMO methods, which have been combined in various ways. This paper proposes a Web Ontology Language (OWL) ontology to model and systematize the knowledge of preference-based multiobjective evolutionary algorithms (PMOEAs). Detailed procedure is given on how to build and use the ontology with the help of Protégé. Different use-cases, including training new learners, querying and reasoning are exemplified and show remarkable benefit for both EMO and MCDM communities.
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    Maximizing Consensus in Portfolio Selection in Multicriteria Group Decision Making
    (Elsevier, 2016-10-04) Emmerich, M. T. M.; Deutz, A.; Li, Longmei; Maulana, A.; Yevseyeva, Iryna
    This paper deals with a scenario of decision making where a moderator selects a (sub)set (aka portfolio) of decision alternatives from a larger set. The larger the number of decision makers who agree on a solution in the portfolio the more successful the moderator is. We assume that decision makers decide independently from each other but indicate their preferences with respect to different objectives in terms of desirability functions, which can be interpreted as cumulative (probability) density functions. A procedure to select a solution with maximal expected number of decision makers that accept it is provided. Moreover, this is generalized to sets of solutions. An algorithm for computing and maximizing the expected number of decision makers that can agree on at least one solution in a subset of decision alternatives is developed. Computational aspects, as well as practical examples for using this for item selection from a database will be discussed.