A clustering method using new modularity indices and a genetic algorithm with extended chromosomes

Sangjin Jung, Timothy W. Simpson

Research output: Contribution to conferencePaper

6 Scopus citations

Abstract

Module definition entails clustering an original product architecture into independent or coordinated modules. Clustering algorithms based on Design Structure Matrices (DSMs) for defining modules have been widely studied. After reviewing existing clustering algorithms, we introduce simple new metrics that can be used as modularity indices bounded between 0 and 1 and also utilized as the objective functions to obtain optimal DSMs including the maximized interactions within modules and the minimized interactions between modules. As a search strategy for clustering modules, a combinatorial genetic algorithm using a new extended chromosome approach and modified operators for the chromosome is suggested. The module definition results indicated that the proposed clustering method using new modularity indices and genetic algorithm helps obtain optimal modular product architectures more logically.

Original languageEnglish (US)
Pages167-176
Number of pages10
DOIs
StatePublished - Jan 1 2014
Event16th International Dependency and Structure Modelling Conference, DSM 2014 - Paris, France
Duration: Jul 2 2014Jul 4 2014

Other

Other16th International Dependency and Structure Modelling Conference, DSM 2014
CountryFrance
CityParis
Period7/2/147/4/14

All Science Journal Classification (ASJC) codes

  • Building and Construction
  • Civil and Structural Engineering

Fingerprint Dive into the research topics of 'A clustering method using new modularity indices and a genetic algorithm with extended chromosomes'. Together they form a unique fingerprint.

  • Cite this

    Jung, S., & Simpson, T. W. (2014). A clustering method using new modularity indices and a genetic algorithm with extended chromosomes. 167-176. Paper presented at 16th International Dependency and Structure Modelling Conference, DSM 2014, Paris, France. https://doi.org/10.3139/9781569904923.017