Matching UML class diagrams using a Hybridized Greedy-Genetic algorithm

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14 Scopus citations

Abstract

Model matching is a fundamental operation for various model management aspects such as model retrieval, evolution, and merging. An accurate matching between the elements of the matched models results in a better model management. This paper presents a Hybridized Greedy-Genetic algorithm for matching UML class diagrams, considering their lexical, internal, and structural similarity. Additionally, using a case study of five class diagrams, the performance of the Hybridized algorithm is empirically compared against the traditional Genetic algorithm in terms of both matching accuracy and convergence time.

Original languageEnglish
Title of host publicationProceedings of the 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies, CSIT 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages161-166
Number of pages6
ISBN (Electronic)9781538616383
DOIs
StatePublished - 6 Nov 2017

Publication series

NameProceedings of the 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies, CSIT 2017
Volume1

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Keywords

  • Hybridized-Greedy-Genetic
  • model matching
  • similarity metrics

ASJC Scopus subject areas

  • Information Systems and Management
  • Management Science and Operations Research
  • Strategy and Management
  • Computer Science Applications

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