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Data mining methods for knowledge discovery in multi-objective optimization: Part A - Survey
Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID-id: 0000-0001-5436-2128
Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID-id: 0000-0003-0111-1776
Department of Electrical and Computer Engineering, Michigan State University, USA.ORCID-id: 0000-0001-7402-9939
2017 (Engelska)Ingår i: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 70, s. 139-159Artikel, forskningsöversikt (Refereegranskat) Published
Abstract [en]

Real-world optimization problems typically involve multiple objectives to be optimized simultaneously under multiple constraints and with respect to several variables. While multi-objective optimization itself can be a challenging task, equally difficult is the ability to make sense of the obtained solutions. In this two-part paper, we deal with data mining methods that can be applied to extract knowledge about multi-objective optimization problems from the solutions generated during optimization. This knowledge is expected to provide deeper insights about the problem to the decision maker, in addition to assisting the optimization process in future design iterations through an expert system. The current paper surveys several existing data mining methods and classifies them by methodology and type of knowledge discovered. Most of these methods come from the domain of exploratory data analysis and can be applied to any multivariate data. We specifically look at methods that can generate explicit knowledge in a machine-usable form. A framework for knowledge-driven optimization is proposed, which involves both online and offline elements of knowledge discovery. One of the conclusions of this survey is that while there are a number of data mining methods that can deal with data involving continuous variables, only a few ad hoc methods exist that can provide explicit knowledge when the variables involved are of a discrete nature. Part B of this paper proposes new techniques that can be used with such datasets and applies them to discrete variable multi-objective problems related to production systems. 

Ort, förlag, år, upplaga, sidor
2017. Vol. 70, s. 139-159
Nyckelord [en]
Data mining, Multi-objective optimization, Descriptive statistics, Visual data mining, Machine learning, Knowledge-driven optimization
Nationell ämneskategori
Datavetenskap (datalogi)
Forskningsämne
Teknik; Produktion och automatiseringsteknik; INF201 Virtual Production Development
Identifikatorer
URN: urn:nbn:se:his:diva-13267DOI: 10.1016/j.eswa.2016.10.015ISI: 000389162000009Scopus ID: 2-s2.0-84995972531OAI: oai:DiVA.org:his-13267DiVA, id: diva2:1060702
Projekt
KDISCO and Knowledge Driven Decision Support via Optimization (KDDS)
Forskningsfinansiär
KK-stiftelsen, 41231Tillgänglig från: 2016-12-29 Skapad: 2016-12-29 Senast uppdaterad: 2019-01-24Bibliografiskt granskad

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