Matching UML class diagrams using a Hybridized Greedy-Genetic algorithm

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...

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Bibliographic Details
Published in2017 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT) Vol. 1; pp. 161 - 166
Main Authors Al-Khiaty, Mojeeb Al-Rhman, Ahmed, Moataz
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2017
Subjects
Biological cells
Clustering algorithms
Computational modeling
Genetic algorithms
Hybridized-Greedy-Genetic
model matching
similarity metrics
Sociology
Statistics
Unified modeling language
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Summary: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.
ISBN:9781538616383
1538616386
DOI:10.1109/STC-CSIT.2017.8098759