Integration of growing self-organizing map and bee colony optimization algorithm for part clustering

Integration of growing self-organizing map and bee colony optimization algorithm for part clustering •Overcome SOMs’ drawback by providing better topology and initial weights using GSOM.•Propose a two-stage clustering algorithm which comprises of GSOM, SOM and metaheuristic.•Apply BCO algorithm to find optimal weights for SOM.•Apply the proposed GSOM + BCOSOM algorithm to real world part clustering problem. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Computers & Industrial Engineering Elsevier

Integration of growing self-organizing map and bee colony optimization algorithm for part clustering

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Publisher
Elsevier
Copyright
Copyright © 2018 Elsevier Ltd
ISSN
0360-8352
eISSN
1879-0550
D.O.I.
10.1016/j.cie.2018.04.044
Publisher site
See Article on Publisher Site

Abstract

•Overcome SOMs’ drawback by providing better topology and initial weights using GSOM.•Propose a two-stage clustering algorithm which comprises of GSOM, SOM and metaheuristic.•Apply BCO algorithm to find optimal weights for SOM.•Apply the proposed GSOM + BCOSOM algorithm to real world part clustering problem.

Journal

Computers & Industrial EngineeringElsevier

Published: Jun 1, 2018

References

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