Skip to main content
SpringerLink
Log in

Hybrid Genetic-Bees Algorithm in Multi-layer Perceptron Optimization

  • Conference paper
  • First Online:
Proceedings of International Conference on Data Science and Applications

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 552))

Abstract

The Multi-layer Perceptron (MLP) is extensively used in solving real-world problems. Backpropagation (BP) is often employed to deal with training data in MLP. However, this method is quickly faced with premature convergence problems and local traps. In this study, the Hybrid Genetic-Bees Algorithms (HGBA) in training MLP is reported for the first time. The genetic algorithm (GA) is used to improve the global search phase of the Bees Algorithm (BA), which is then to search for better solutions. The proposed HGBA is tested on four standard UCI classification datasets with different levels of difficulty. Experimental results show that HGBA provides significantly better performance than Particle Swarm Optimization (PSO) in training MLP with higher accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 219.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 279.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. de Jesus Rubio J (2009) SOFMLS: online self-organizing fuzzy modified least-squares network. IEEE Trans Fuzzy Syst 17(6):1296–1309. https://ieeexplore.ieee.org/document/5196829/

  2. Nguyen G, Dlugolinsky S, Bobák M, Tran V, López García Á, Heredia I, Malík P, Hluchý L (2019) Machine learning and deep learning frameworks and libraries for large-scale data mining: a survey. Artif Intell Rev 52(1):77–124. https://doi.org/10.1007/s10462-018-09679-z

  3. Chinas P, Lopez I, Vazquez JA, Osorio R, Lefranc G (2015) SVM and ANN application to multivariate pattern recognition using scatter data. IEEE Latin Am Trans 13(5):1633–1639

    Google Scholar 

  4. Majidi M, Fadali MS, Etezadi-Amoli M, Oskuoee M (2015) Partial discharge pattern recognition via sparse representation and ANN. IEEE Trans Dielectr Electr Insul 22(2):1061–1070

    Google Scholar 

  5. Kermadi M, Berkouk EM (2017) Artificial intelligence-based maximum power point tracking controllers for photovoltaic systems: comparative study. Renew Sustain Energy Rev 69:369–386

    Article  Google Scholar 

  6. Lee HH, Phuong LM, Dzung PQ, Dan Vu NT, Khoa LD (2010) The new maximum power point tracking algorithm using ANN-based solar PV systems. In: TENCON 2010–2010 IEEE region 10 conference, pp 2179–2184. ISSN: 2159-3450

    Google Scholar 

  7. Ramaprabha R, Gothandaraman V, Kanimozhi K, Divya R, Mathur BL (2011) Maximum power point tracking using GA-optimized artificial neural network for solar PV system. In: 2011 1st international conference on electrical energy systems, pp 264–268

    Google Scholar 

  8. Egmont-Petersen M, de Ridder D, Handels H (2002) Image processing with neural networks—a review. Pattern Recogn 35(10):2279–2301 (2002). https://linkinghub.elsevier.com/retrieve/pii/S0031320301001789

  9. Mendes R, Cortez P, Rocha M, Neves J (2002) Particle swarms for feedforward neural network training. In: Proceedings of the 2002 international joint conference on neural networks. In: IJCNN’02 (Cat. No. 02CH37290). IEEE, Honolulu, HI, USA, pp 1895–1899. http://ieeexplore.ieee.org/document/1007808/

  10. Zhao ZQ, Zheng P, Xu ST, Wu X (2019) Object detection with deep learning: a review. IEEE Trans Neural Netw Learn Syst 30(11):3212–3232

    Google Scholar 

  11. Oliver J, Baxter R, Wallace C (1996) Unsupervised learning using MML. In: Machine learning: proceedings of the thirteenth international conference (ICML 96). Morgan Kaufmann Publishers, pp 364–372

    Google Scholar 

  12. Meng X, Jiang J, Wang H (2021) AGWO: advanced GWO in multi-layer perception optimization. Exp Syst Appl 173:114676. https://linkinghub.elsevier.com/retrieve/pii/S0957417421001172

  13. Lecun Y (2001) A theoretical framework for back-propagation

    Google Scholar 

  14. Rumelhart DE, Hinton GE, Williams RJ (1985) Learning internal representations by error propagation. Technical reports. Institute for Cognitive Science, California University, San Diego, La Jolla. https://apps.dtic.mil/sti/citations/ADA164453

  15. Vogl TP, Mangis JK, Rigler AK, Zink WT, Alkon DL (1988) Accelerating the convergence of the back-propagation method. Biol Cybernet 59(4):257–263. https://doi.org/10.1007/BF00332914

  16. Pham D, Mahmuddin M, Otri S, Al-Jabbouli H (2007) Application of the bees algorithm to the selection features for manufacturing data

    Google Scholar 

  17. Sharma K, Gupta P, Sharma H (2016) Fully informed artificial bee colony algorithm. J Exp Theoret Artif Intell 28(1–2):403–416

    Article  Google Scholar 

  18. Sharma H, Bansal JC, Arya K, Yang XS (2016) Lévy flight artificial bee colony algorithm. Int J Syst Sci 47(11):2652–2670

    Article  MATH  Google Scholar 

  19. Jadon SS, Bansal JC, Tiwari R, Sharma H (2015) Accelerating artificial bee colony algorithm with adaptive local search. Memetic Comput 7(3):215–230

    Article  Google Scholar 

  20. Ding S, Su C, Yu J (2011) An optimizing BP neural network algorithm based on genetic algorithm. Artif Intell Rev 36(2):153–162. https://doi.org/10.1007/s10462-011-9208-z

  21. Wolpert D, Macready W (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82

    Google Scholar 

  22. Packianather MS, Yuce B, Mastrocinque E, Fruggiero F, Pham DT, Lambiase A (2014) Novel genetic Bees algorithm applied to single machine scheduling problem. In: 2014 world automation congress (WAC), pp 906–911. ISSN: 2154-4824

    Google Scholar 

  23. Pham DT, Afify A, Koc E (2007) Manufacturing cell formation using the bees algorithm. In: Innovative production machines and systems virtual conference. Cardiff, UK

    Google Scholar 

  24. Lambiase A, Iannone R, Miranda S, Lambiase A, Pham D (2016) Bees algorithm for effective supply chains configuration. Int J Eng Bus Manag 8:1847979016675301

    Article  Google Scholar 

  25. Pham D, Ghanbarzadeh A, Koc E, Otri S, Rahim S, Zaidi M (2005) The bees algorithm-technical report. Manufacturing Engineering Centre, Cardiff University, Cardiff

    Google Scholar 

  26. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95-international conference on neural networks, vol 4. IEEE, pp 1942–1948

    Google Scholar 

  27. Yuce B, Packianather MS, Mastrocinque E, Pham DT, Lambiase A (2013) Honey bees inspired optimization method: the bees algorithm. Insects 4(4):646–662

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Industrial Engineering and Management, International University, Vietnam National University, Block 6, Linh Trung Ward, Thu Duc District, Ho Chi Minh City, Vietnam

    Truong Tran Mai Anh & Tran Duc Vi

Authors
  1. Truong Tran Mai Anh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tran Duc Vi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tran Duc Vi .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering and Information Technology, Jaypee Institute of Information Technology, Noida, India

    Mukesh Saraswat

  2. Department of Computer Science and Engineering, Jadavpur University, Kolkata, India

    Chandreyee Chowdhury

  3. Department of Computer Science and Engineering, Jadavpur University, Kolkata, India

    Chintan Kumar Mandal

  4. Data Science Institute, University of Technology Sydney, Sydney, NSW, Australia

    Amir H. Gandomi

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Anh, T.T.M., Vi, T.D. (2023). Hybrid Genetic-Bees Algorithm in Multi-layer Perceptron Optimization. In: Saraswat, M., Chowdhury, C., Kumar Mandal, C., Gandomi, A.H. (eds) Proceedings of International Conference on Data Science and Applications. Lecture Notes in Networks and Systems, vol 552. Springer, Singapore. https://doi.org/10.1007/978-981-19-6634-7_11

Download citation

Publish with us

Policies and ethics

Search

Navigation