A New Internal Architecture Based on Feature Selection for Holonic Manufacturing System
This paper suggests a new internal architecture of
holon based on feature selection model using the combination of
Bees Algorithm (BA) and Artificial Neural Network (ANN). BA is
used to generate features while ANN is used as a classifier to
evaluate the produced features. Proposed system is applied on the
Wine dataset, the statistical result proves that the proposed system is
effective and has the ability to choose informative features with high
accuracy.
[1] A. Koestler, “The Ghost in the Machine”. London: Arkana Books; 1971.
[2] L. Paulo and R. Francisco, “ADACOR: A holonic architecture for agile
and adaptive manufacturing”, Computers in Industry, Vol. 57, No 2,
2006, pp. 121-130.
[3] L. Paulo, "Holonic Rationale and Self-organization on Design of
Complex Evolvable Systems, Holonic and Multi-Agent Systems for
Manufacturing", Lecture Notes in Artificial Intelligence, Vol. 5696,
Springer-Verlag, 2009, pp. 11–23.
[4] L. Paulo and R. Francisco, “Towards autonomy, self-organization and
learning in holonic manufacturing”, Multi-Agent Systems and
Applications III, Lecture Notes in Artificial Intelligence, Vol. 2691,
Springer, 2003, pp. 544–553.
[5] L. Bongaerts, “Integration of Scheduling and Control in Holonic
Manufacturing Systems”, PhD Thesis, Production and Automation
Division, Katholieke Universiteit Leuven, Leuven, Belgium, 1998.
[6] S. Weiming, J. Hyun, H. Douglas, “Application of agent-based systems
in intelligent manufacturing: An updated review”, Advanced
Engineering Informatics, ELSEVIR, 2006, P. 415–431.
[7] B. Vicente and G. Adriana, A Multi-agent Methodology for Holonic
Manufacturing Systems, PhD thesis, 2008.
[8] J. Christensen, “Holonic Manufacturing Systems: Initial Architecture
and Standards Directions”, In: Proceedings of First European
Conference on Holonic Manufacturing Systems, European HMS
Consortium. Hanover; 1994. – P. 1–20.
[9] J. Christensen, “HMS/FB Architecture and its Implementation, Agent
Based Manufacturing: Advances in the Holonic Approach”. Berlin:
Springer, 2003. – P. 53–87.
[10] B. Van, W. Jo, Paul V., B. Luc, “Reference architecture for Holonic
manufacturing systems: PROSA”, Computers in Industry - Special Issue
on Intelligent Manufacturing Systems, 1998, pp. 255–276. [11] L. Paulo and Francisco R., “An Agile and Adaptive Holonic
Architecture for Manufacturing Contro", scientific area of Industrial
Automation, PhD thesis, 2004.
[12] L. Qing, L. Dongbo, and H. Dao, “Methods to Support Holon
Aggregation in Holonic Manufacturing System”, International
Symposium on Computational Intelligence and Design, 2008.
[13] L. Paulo, "Holonic Rationale and Self-organization on Design of
Complex Evolvable Systems, Holonic and Multi-Agent Systems for
Manufacturing", Lecture Notes in Artificial Intelligence, Vol. 5696,
Springer-Verlag, 2009, pp. 11–23.
[14] D. Pham and A. Ghanbarzadeh, E. Koc, S. Otri, S. Rahim, M. Zaidi.,
“The bees algorithm, a novel tool for complex optimization problems”,
In: Proc. of the 2nd international virtual conference on intelligent
production machines and systems, Cardiff, UK, pp. 454-459, 2006.
[15] D. Pham and A. Ghanbarzadeh, “Multi-objective optimization is using
the bees algorithm”, In: proceedings of the 3rd international virtual
conference on intelligent production machines and systems, Scotland,
2007.
[16] A. Fahmy., “Using the Bees Algorithm to select the optimal speed
parameters for wind turbine generators”, Journal of King Saud
University – Computer and Information Sciences (2012) 24, 17–26.
[17] D. Pham, “The Bees Algorithm. Technical Note”, Manufacturing
Engineering Centre, Cardiff University, UK, 2005.
[18] A. Siti, “A Study of Search Neighbourhood in the Bees Algorithm”,
PhD Thesis, Manufacturing Engineering Centre, School of Engineering,
Cardiff University, United Kingdom, 2012.
[19] S. Mohammad, and S. Shima, “A Hybrid Approach for Effective Feature
Selection using Neural Networks and Artificial Bee Colony
Optimization”, International Conference on Machine Vision, ICMV
,2010.
[20] J. Zurada, “Introduction to Artificial Neural Systems”, Prentice Hall
International, Inc, 1996.
[21] The UCI machine learning repository, http://archive.ics.uci.edu/ml.
[1] A. Koestler, “The Ghost in the Machine”. London: Arkana Books; 1971.
[2] L. Paulo and R. Francisco, “ADACOR: A holonic architecture for agile
and adaptive manufacturing”, Computers in Industry, Vol. 57, No 2,
2006, pp. 121-130.
[3] L. Paulo, "Holonic Rationale and Self-organization on Design of
Complex Evolvable Systems, Holonic and Multi-Agent Systems for
Manufacturing", Lecture Notes in Artificial Intelligence, Vol. 5696,
Springer-Verlag, 2009, pp. 11–23.
[4] L. Paulo and R. Francisco, “Towards autonomy, self-organization and
learning in holonic manufacturing”, Multi-Agent Systems and
Applications III, Lecture Notes in Artificial Intelligence, Vol. 2691,
Springer, 2003, pp. 544–553.
[5] L. Bongaerts, “Integration of Scheduling and Control in Holonic
Manufacturing Systems”, PhD Thesis, Production and Automation
Division, Katholieke Universiteit Leuven, Leuven, Belgium, 1998.
[6] S. Weiming, J. Hyun, H. Douglas, “Application of agent-based systems
in intelligent manufacturing: An updated review”, Advanced
Engineering Informatics, ELSEVIR, 2006, P. 415–431.
[7] B. Vicente and G. Adriana, A Multi-agent Methodology for Holonic
Manufacturing Systems, PhD thesis, 2008.
[8] J. Christensen, “Holonic Manufacturing Systems: Initial Architecture
and Standards Directions”, In: Proceedings of First European
Conference on Holonic Manufacturing Systems, European HMS
Consortium. Hanover; 1994. – P. 1–20.
[9] J. Christensen, “HMS/FB Architecture and its Implementation, Agent
Based Manufacturing: Advances in the Holonic Approach”. Berlin:
Springer, 2003. – P. 53–87.
[10] B. Van, W. Jo, Paul V., B. Luc, “Reference architecture for Holonic
manufacturing systems: PROSA”, Computers in Industry - Special Issue
on Intelligent Manufacturing Systems, 1998, pp. 255–276. [11] L. Paulo and Francisco R., “An Agile and Adaptive Holonic
Architecture for Manufacturing Contro", scientific area of Industrial
Automation, PhD thesis, 2004.
[12] L. Qing, L. Dongbo, and H. Dao, “Methods to Support Holon
Aggregation in Holonic Manufacturing System”, International
Symposium on Computational Intelligence and Design, 2008.
[13] L. Paulo, "Holonic Rationale and Self-organization on Design of
Complex Evolvable Systems, Holonic and Multi-Agent Systems for
Manufacturing", Lecture Notes in Artificial Intelligence, Vol. 5696,
Springer-Verlag, 2009, pp. 11–23.
[14] D. Pham and A. Ghanbarzadeh, E. Koc, S. Otri, S. Rahim, M. Zaidi.,
“The bees algorithm, a novel tool for complex optimization problems”,
In: Proc. of the 2nd international virtual conference on intelligent
production machines and systems, Cardiff, UK, pp. 454-459, 2006.
[15] D. Pham and A. Ghanbarzadeh, “Multi-objective optimization is using
the bees algorithm”, In: proceedings of the 3rd international virtual
conference on intelligent production machines and systems, Scotland,
2007.
[16] A. Fahmy., “Using the Bees Algorithm to select the optimal speed
parameters for wind turbine generators”, Journal of King Saud
University – Computer and Information Sciences (2012) 24, 17–26.
[17] D. Pham, “The Bees Algorithm. Technical Note”, Manufacturing
Engineering Centre, Cardiff University, UK, 2005.
[18] A. Siti, “A Study of Search Neighbourhood in the Bees Algorithm”,
PhD Thesis, Manufacturing Engineering Centre, School of Engineering,
Cardiff University, United Kingdom, 2012.
[19] S. Mohammad, and S. Shima, “A Hybrid Approach for Effective Feature
Selection using Neural Networks and Artificial Bee Colony
Optimization”, International Conference on Machine Vision, ICMV
,2010.
[20] J. Zurada, “Introduction to Artificial Neural Systems”, Prentice Hall
International, Inc, 1996.
[21] The UCI machine learning repository, http://archive.ics.uci.edu/ml.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:70962", author = "Jihan Abdulazeez Ahmed and Adnan Mohsin Abdulazeez Brifcani", title = "A New Internal Architecture Based on Feature Selection for Holonic Manufacturing System", abstract = "This paper suggests a new internal architecture of
holon based on feature selection model using the combination of
Bees Algorithm (BA) and Artificial Neural Network (ANN). BA is
used to generate features while ANN is used as a classifier to
evaluate the produced features. Proposed system is applied on the
Wine dataset, the statistical result proves that the proposed system is
effective and has the ability to choose informative features with high
accuracy.", keywords = "Artificial Neural Networks, Holonic Approach,
Feature Selection, Bee Algorithm.", volume = "9", number = "8", pages = "1549-4", }
