Soft Computing

, Volume 23, Issue 1, pp 285–303 | Cite as

Multi-strategy learning and deep harmony memory improvisation for self-organizing neurons

  • Shafaatunnur HasanEmail author
  • Siti Mariyam Shamsuddin
Methodologies and Application


This study proposes a concept of representation learning by implementing multi-strategy deep learning harmony memory improvisation for selecting the best harmony of self-organizing neurons. Representation learning is a set of methods that allows a machine to be fed with raw data and to automatically discover the representations needed for detection or classification. In our study, the deep multi-strategy learning involves the convolution of the self-organizing neurons with deep harmony memories improvisation in self-organizing and representation of map learning. The convolution of self-organizing neurons and harmony memory optimize the representation neurons’ weights by generating the optimal best matching unit which is represented as fitness function of \(f_1 \left( x \right) \) and \(f_2 \left( x \right) \). While \(f_1 \left[ {g\left( {{f}''_1 \left( x \right) } \right) } \right] \) and \(f_2 \left[ {g\left( {{f}'_2 \left( x \right) } \right) } \right] \) represent the New Harmony fitness function. The best fitness function, \(f_{{ best}} (x)\) is selected based on the \(f_1 \left( x \right) \) and \(f_2 \left( x \right) \) performance which will be later stored in the harmony memory vector. The position vector of a particle is subjected to the Newtonian mechanics constant acceleration during the interval \(\Delta t\). The search space of self-organizing map with Newton-based particle swarm algorithm particles depends on the width area, \(\sigma _\alpha (t)\) of organizing neurons lattice structure. Our proposed methods are experimented on various biomedical datasets. The findings indicate that the proposed methods provide better quantization error for clustering and good classification accuracy with statistical measurement validations.


Representation learning Machine learning Deep learning Meta-heuristic algorithms Harmony search algorithm Classification and clustering problems 



The authors would like to thank Ministry of Higher Education (MOHE) and Universiti Teknologi Malaysia (UTM) for their support in Research and Development, UTM Big Data Centre and the Soft Computing Research Group (SCRG) for the inspiration in making this study a success. This work is partially supported by the UTM Research University Grant Scheme (17H62) and FRGS (4F786).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This paper does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.


  1. Alcalá-Fdez J, Fernandez A, Luengo J, Derrac J, García S, Sánchez L, Herrera F (2011) KEEL data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. J Mult Valued Log Soft Comput 17(2–3):255–287Google Scholar
  2. Alia O, Mandava R (2011) The variants of the harmony search algorithm: an overview. Artif Intell Rev 36(1):49–68Google Scholar
  3. Amiri B, Hossain L, Mosavi SE (2010) Application of harmony search algorithm on clustering. In World congress on engineering and computer science, vols 1 and 2, pp 460–465Google Scholar
  4. Beheshti Z, Shamsuddin SM (2013) A review of population-based meta-heuristic algorithms. Int J Adv Soft Comput Appl 5(1):1–35Google Scholar
  5. Beheshti Z, Shamsuddin SMH (2014) CAPSO: centripetal accelerated particle swarm optimization. Inf Sci 258:54–79MathSciNetGoogle Scholar
  6. Bahesti Z, Shamsuddin SM, Hasan S (2013) MPSO: median oriented particle swarm optimization. Appl Math Comput 219:5817–5836MathSciNetzbMATHGoogle Scholar
  7. Beheshti Z, Shamsuddin SMH, Beheshti E, Yuhaniz SS (2014) Enhancement of artificial neural network learning using centripetal accelerated particle swarm optimization for medical diseases diagnosis. Soft Comput 18(11):2253–2270Google Scholar
  8. Brabazon A, O’Neill M (2006) Biologically inspired algorithms for financial modeling. Springer, BerlinzbMATHGoogle Scholar
  9. David HW, William GM (1997) No free lunch theorem for optimization. IEEE Trans Evolut Comput 1(1):67–82Google Scholar
  10. Geem ZW (2009a) Particle-swarm harmony search for water network design. Eng Optim 41(4):297–311Google Scholar
  11. Geem ZW (2009b) Music inspired harmony search algorithm: theory and applications. Springer, New YorkGoogle Scholar
  12. Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68Google Scholar
  13. Hasan S (2010) Enhanced self-organizing map with particle swarm optimization for classification problems. Master thesis, Universiti Teknologi MalaysiaGoogle Scholar
  14. Hasan S, Shamsuddin SM (2011) Multistrategy self-organizing map learning for classification problems. Comput Intell Neurosci 2011(121787):11. Google Scholar
  15. Haykin S (1999) Neural networks: a comprehensive foundation, 2nd edn. Prentice Hall, Upper Saddle RiverzbMATHGoogle Scholar
  16. Jiming L, Tsui KC (2006) Toward nature-inspired computing. Commun ACM 49(10):59–64Google Scholar
  17. Kattan A, Abdullah R, Salam RA (2010) Harmony search based supervised training of artificial neural networks. In: UKSim-AMSS first international conference on intelligent systems, modelling and simulation, pp 105–110Google Scholar
  18. Kennedy J, Eberhart (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks. IEEE Press, Piscataway, NJ, pp 1942–1948Google Scholar
  19. Kennedy J, Eberhart RC, Shi Y (2001) Swarm intelligence. Morgan Kaufmann, San FranciscoGoogle Scholar
  20. Kohonen T (2001) Self-organizing maps. Springer series in information sciences, vol 30, 3rd extended edition. Springer, BerlinGoogle Scholar
  21. Kulluk S, Ozbakir L, Baykasoglu A (2012) Training neural networks with harmony search algorithms for classification problems. Eng Appl Artif Intell 25(1):11–19Google Scholar
  22. Lee Anzy, Geem Zong Woo, Suh Kyung-Duck (2016) Determination of optimal weights of an artificial neural networks by using the harmony search algorithm: application to breakwater armor stones. Appl Sci 6(164):1–17Google Scholar
  23. Mahdavi M, Fesanghary M, Damangir E (2007) An improved harmony search algorithm for solving optimization problems. Appl Math Comput 188(2):1567–1579MathSciNetzbMATHGoogle Scholar
  24. Mahdavi M, Chehreghani MH, Abolhassani H, Forsati R (2008) Novel meta-heuristic algorithms for clustering web documents. Appl Math Comput 201(1–2):441–451MathSciNetzbMATHGoogle Scholar
  25. Michalski RS (1994) Machine learning: a multistrategy approach. In: Michalski RS, Tecuci G (eds) Inferential learning theory as conceptual basis for multistrategy learning, vol IV. Morgan Kaufmann Publishers, San MateoGoogle Scholar
  26. Negnevitsky M (2005) Artificial intelligence: a guide to intelligent systems, 2nd edn. Addison Wesley, HarlowGoogle Scholar
  27. Omran MGH, Mahdavi M (2008) Global-best harmony search. Appl Math Comput 198(2):643–656MathSciNetzbMATHGoogle Scholar
  28. O’Neill M, Brabazon A (2008) Self-organising swarm (SOSwarm). Soft Comput 12(11):1073–1080Google Scholar
  29. Ozcift A, Kaya M, Gulten A, Karabulut M (2009) Swarm optimized organizing map (SWOM): a swarm intelligence based optimization of self-organizing map. Expert Syst Appl 36(7):10640–10648Google Scholar
  30. Pan Q-K, SuganthanM PN, Tasgetiren F, Liang JJ (2010) A self-adaptive global best harmony search algorithm for continuous optimization problems. Appl Math Comput 216(3):830–848MathSciNetzbMATHGoogle Scholar
  31. Von der Malsburg C (1973) Self-organization of orientation sensitive cells in the striate cortex. Kybernetik 14:85–100Google Scholar
  32. Xiao X, Dow ER, Eberhart R, Miled ZB, Oppelt RJ (2003) Gene-clustering using self-organizing maps and particle swarm optimization. IEEE international parallel and distributed processing symposium (IPDPS). IEEE PressGoogle Scholar
  33. Xiao X, Dow ER, Eberhart R, Miled ZB, Oppelt RJ (2004) A hybrid self-organizing maps and particle swarm optimization approach. Concurr Comput Pract Exp 16(9):895–915Google Scholar
  34. Yang XS (2009) Music-inspired Harmony search algorithm. In: Geem Z (ed) Harmony search as a metaheuristic algorithm. Springer, Berlin, pp 1–14Google Scholar
  35. Yang XS (2010) Engineering optimization: an introduction with metaheuristic applications. Wiley, HobokenGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Shafaatunnur Hasan
    • 1
    • 2
    Email author
  • Siti Mariyam Shamsuddin
    • 1
    • 2
  1. 1.UTM Big Data CentreUniversiti Teknologi MalaysiaSkudaiMalaysia
  2. 2.Faculty of ComputingUniversiti Teknologi MalaysiaSkudaiMalaysia

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