Skip to main content
Springer Nature Link
Log in

A Teaching-Learning-Based Optimization with Modified Learning Phases for Continuous Optimization

  • Conference paper
  • First Online:
Intelligent Computing (SAI 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1229))

Included in the following conference series:

  • 824 Accesses

Abstract

The deviation between the modelling of teaching-learning based optimization (TLBO) framework and actual scenario of classroom teaching and learning process is considered as one factor which contributes to the imbalance of algorithm’s exploration and exploitation searches, hence restricting its search performance. In this paper, the TLBO with modified learning phases (TLBO-MLPs) is proposed to achieve better search performance of algorithm through the further refinement of learning framework so that it can reflect the actual teaching and learning processes in classroom more accurately. A modified teacher phase is first introduced in TLBO-MLPs, where each learner is modelled to have different perspectives of mainstream knowledge in classroom to maintain the diversity of population’s knowledge. A modified learner phase consisting of an adaptive peer learning mechanism and a self-learning mechanism are also proposed in TLBO-MLPs. The former mechanism enables each learner to interact with multiple learners in gaining new knowledge for different subjects, while the latter facilitates the update of new knowledge through personal efforts. The overall performances of TLBO-MLPs in solving the CEC 2014 test functions are compared with seven competitors. Extensive simulation results show that TLBO-MLPs has demonstrated the best search performance among all compared methods in solving majority of test functions.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Whitley, D., Sutton, A.M.: Genetic algorithms — a survey of models and methods. In: Rozenberg, G., Bäck, T., Kok, J.N. (eds.) Handbook of Natural Computing, pp. 637–671. Springer, Heidelberg (2012)

    Google Scholar 

  2. Kramer, O.: Evolutionary self-adaptation: a survey of operators and strategy parameters. Evol. Intell. 3(2), 51–65 (2010)

    MATH  Google Scholar 

  3. Burke, E., Gustafson, S., Kendall, G.: A survey and analysis of diversity measures in genetic programming. In: Proceedings of the 4th Annual Conference on Genetic and Evolutionary Computation, New York (2002)

    Google Scholar 

  4. Das, S., Suganthan, P.N.: Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15(1), 4–31 (2011)

    Google Scholar 

  5. Valle, Y.D., Venayagamoorthy, G.K., Mohagheghi, S., Hernandez, J., Harley, R.G.: Particle swarm optimization: basic concepts, variants and applications in power systems. IEEE Trans. Evol. Comput. 12(2), 171–195 (2008)

    Google Scholar 

  6. Dorigo, M., Blum, C.: Ant colony optimization theory: a survey. Theor. Comput. Sci. 344(2–3), 243–278 (2005)

    MathSciNet  MATH  Google Scholar 

  7. Karaboga, D., Gorkemli, B., Ozturk, C., Karaboga, N.: A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif. Intell. Rev. 42(1), 21–57 (2014)

    Google Scholar 

  8. Faris, H., Aljarah, I., Al-Betar, M.A.: Mirjalili, S,: Grey wolf optimizer: a review of recent variants and applications. Neural Comput. Appl. 30(2), 413–435 (2018)

    Google Scholar 

  9. Ang, C.K., Tang, S.H., Mashohor, S., Ariffin, M.K.A.M., Khaksar, W.: Solving continuous trajectory and forward kinematics simultaneously based on ANN. Int. J. Comput. Commun. Control 9(3), 253–260 (2014)

    Google Scholar 

  10. Alrifaey, M., Tang, S.H., Supeni, E.E., As’arry, A., Ang, C.K.: Identification and priorization of risk factors in an electrical generator based on the hybrid FMEA framwork. Energies 12(4), 649 (2019)

    Google Scholar 

  11. Lim, W.H., Isa, N.A.M.: Particle swarm optimization with dual-level task allocation. Eng. Appl. Artif. Intell. 38, 88–110 (2015)

    Google Scholar 

  12. Yao, L., Shen, J.Y., Lim, W.H.: Real-time energy management optimization for smart household. In: 2016 IEEE International Conference on Internet of Things (iThings), Chengdu, China, pp. 20–26 (2016)

    Google Scholar 

  13. Yao, L., Damiran, Z., Lim, W.H.: Energy management optimization scheme for smart home considering different types of appliances. In: 2017 IEEE International Conference on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe), Milan, Italy, pp. 1–6 (2017)

    Google Scholar 

  14. Solihin, M.I., Akmeliawati, R., Muhida, R., Legowo, A.: Guaranteed robust state feedback controller via constrained optimization using differential evolution. In: 6th International Colloquium on Signal Processing & its Applications, pp. 1–6 (2010)

    Google Scholar 

  15. Solihin, M.I., Wahyudi, Akmeliawati, R.: PSO-based optimization of state feedback tracking controller for a flexible link manipulator. In: International Conference of Soft Computing and Pattern Recognition, pp. 72–76 (2009)

    Google Scholar 

  16. Lim, W.H., Isa, N.A.M., Tiang, S.S., Tan, T.H., Natarajan, E., Wong, C.H., Tang, J.R.: Self-adaptive topologically connected-based particle swarm optimization. IEEE Access 6, 65347–65366 (2018)

    Google Scholar 

  17. Sathiyamoorthy, V., Sekar, T., Natarajan, E.: Optimization of processing parameters in ECM of die tool steel using nanofluid by multiobjective genetic algorithm. Sci. World J. 2015, 6 (2015)

    Google Scholar 

  18. Yao, L., Lim, W.H., Tiang, S.S., Tan, T.H., Wong, C.H., Pang, J.Y.: Demand bidding optimization for an aggregator with a genetic algorithm. Enegies 11(10), 2498 (2018)

    Google Scholar 

  19. Yao, L., Damiran, Z., Lim, W.H.: A fuzzy logic based charging scheme for electric vehicle parking station. In: 2016 IEEE 16th International Conference on Environment and Electrical Engineering, Florence, Italy (2016)

    Google Scholar 

  20. Rao, R.V., Savsani, V.J., Vakharia, D.P.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput. Aided Des. 43(3), 303–315 (2011)

    Google Scholar 

  21. Zou, F., Chen, D., Xu, Q.: A survey of teaching–learning-based optimization. Neurocomputing 335, 366–383 (2019)

    Google Scholar 

  22. Natarajan, E., Kaviarasan, V., Lim, W.H., Tiang, S.S., Parasuraman, S., Elango, S.: Non-dominated sorting modified teaching-learning-based optimization for multi-objective machining of polytetrafluoroethylene (PTFE). J. Intell. Manuf. 31, 911–935 (2020)

    Google Scholar 

  23. Rao, R.V., Waghmare, G.G.: Multi-objective design optimization of a plate-fin heat sink using a teaching-learning-based optimization algorithm. Appl. Therm. Eng. 76, 521–529 (2015)

    Google Scholar 

  24. Natarajan, E., Kaviarasan, V., Lim, W.H., Tiang, S.S., Tan, T.H.: Enhanced multi-objective teaching-learning-based optimization for machining of Delrin. IEEE Access 6, 51528–51546 (2018)

    Google Scholar 

  25. Yua, K., Wang, X., Wang, Z.: Constrained optimization based on improved teaching–learning-based optimization algorithm. Inf. Sci. 352–353, 61–78 (2016)

    Google Scholar 

  26. Savsani, V.J., Tejani, G.G., Patel, V.K.: Truss topology optimization with static and dynamic constraints using modified subpopulation teaching–learning-based optimization. Eng. Optim. 48(11), 1990–2006 (2016)

    MathSciNet  Google Scholar 

  27. Zheng, H., Wang, L., Zheng, X.: Teaching–learning-based optimization algorithm for multi-skill resource constrained project scheduling problem. Soft. Comput. 21(6), 1537–1548 (2017)

    Google Scholar 

  28. Akhtar, J., Koshul, B., Awais, M.: A framework for evolutionary algorithms based on charles sanders peirce’s evolutionary semiotics. Inf. Sci. 236, 93–108 (2013)

    MathSciNet  MATH  Google Scholar 

  29. Satapathy, S.C., Naik, A., Parvathi, K.: Weighted teaching-learning-based optimization for global function optimization. Appl. Math. 4(3), 429–439 (2013)

    Google Scholar 

  30. Cao, J., Luo, J.: A study on SVM based on the weighted elitist teaching-learning-based optimization and application in the fault diagnosis of chemical process. MATEC Web Conf. 22, 05016 (2015)

    Google Scholar 

  31. Li, G., Niu, P., Zhang, W., Liu, Y.: Model NOx emissions by least squares support vector machine with tuning based on ameliorated teaching–learning-based optimization. Chemom. Intell. Lab. Syst. 126, 11–20 (2013)

    Google Scholar 

  32. Wu, Z.-S., Fu, W.-P., Xue, R.: Nonlinear inertia weighted teaching-learning-based optimization for solving global optimization problem. Comput. Intell. Neurosci. 2015(292576), 15 (2015)

    Google Scholar 

  33. Chen, D., Lu, R., Zou, F., Li, S.: Teaching-learning-based optimization with variable-population scheme and its application for ANN and global optimization. Neurocomputing 173, 1096–1111 (2016)

    Google Scholar 

  34. Wang, L., Zou, F., Hei, X., Yang, D., Chen, D., Jiang, Q.: An improved teaching–learning-based optimization with neighborhood search for applications of ANN. Neurocomputing 143, 231–247 (2014)

    Google Scholar 

  35. Chen, D., Zou, F., Li, Z., Wang, J., Li, S.: An improved teaching–learning-based optimization algorithm for solving global optimization problem. Inf. Sci. 297, 171–190 (2015)

    Google Scholar 

  36. Zou, F., Wang, L., Hei, X., Chen, D., Yang, D.: Teaching–learning-based optimization with dynamic group strategy for global optimization. Inf. Sci. 273, 112–131 (2014)

    Google Scholar 

  37. Zhai, Z., Li, S., Liu, Y., Li, Z.: Teaching-learning-based optimization with a fuzzy grouping learning strategy for global numerical optimization. J. Intell. Fuzzy Syst. 29(6), 2345–2356 (2015)

    Google Scholar 

  38. Reddy, S.S.: Clustered adaptive teaching–learning-based optimization algorithm for solving the optimal generation scheduling problem. Electr. Eng. 100(1), 333–346 (2018)

    Google Scholar 

  39. Li, M., Ma, H., Gu, B.: Improved teaching–learning-based optimization algorithm with group learning. J. Intell. Fuzzy Syst. 31(4), 2101–2108 (2016)

    Google Scholar 

  40. Zou, F., Chen, D., Lu, R., Wang, P.: Hierarchical multi-swarm cooperative teaching–learning-based optimization for global optimization. Soft. Comput. 21(23), 6983–7004 (2017)

    Google Scholar 

  41. Shao, W., Pi, D., Shao, Z.: An extended teaching-learning based optimization algorithm for solving no-wait flow shop scheduling problem. Appl. Soft Comput. 61, 193–210 (2017)

    Google Scholar 

  42. Satapathy, S.C., Naik, A.: Modified teaching–learning-based optimization algorithm for global numerical optimization—a comparative study. Swarm Evol. Comput. 16, 28–37 (2014)

    Google Scholar 

  43. Zou, F., Wang, L., Hei, X., Chen, D.: Teaching-learning-based optimization with learning experience of other learners and its application. Appl. Soft Comput. 37, 725–736 (2015)

    Google Scholar 

  44. Zou, F., Wang, L., Chen, D., Hei, X.: An improved teaching-learning-based optimization with differential learning and its application. Math. Probl. Eng. 2015(754562), 19 (2015)

    Google Scholar 

  45. Wang, L., et al.: A hybridization of teaching–learning-based optimization and differential evolution for chaotic time series prediction. Neural Comput. Appl. 25(6), 1407–1422 (2014)

    Google Scholar 

  46. Chen, X., Xu, B., Yu, K., Du, W.: Teaching-learning-based optimization with learning enthusiasm mechanism and its application in chemical engineering. J. Appl. Math. 2018(1806947), 19 (2018)

    Google Scholar 

  47. Chen, X., Yu, K., Du, W., Zhao, W., Liu, G.: Parameters identification of solar cell models using generalized oppositional teaching learning based optimization. Energy 99, 170–180 (2016)

    Google Scholar 

  48. Liang, J.J., Qu, B.Y., Suganthan, P.N.: Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. Zhengzhou University, Zhengzhou China Computational Intelligence Laboratory (2013)

    Google Scholar 

  49. García, S., Molina, D., Lozano, M., Herrera, F.: A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 special session on real parameter optimization. J. Heuristics 15(6), 617 (2008)

    MATH  Google Scholar 

  50. Derrac, J., García, S., Molina, D., Herrera, F.: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol. Comput. 1(1), 3–18 (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Engineering, Technology and Built Environment, UCSI University, 56000, Kuala Lumpur, Malaysia

    Onn Ting Chong, Wei Hong Lim, Koon Meng Ang, Sew Sun Tiang & Chun Kit Ang

  2. School of Electrical and Electronic Engineering, Universiti Sains Malaysia, 14300, Nibong Tebal, Malaysia

    Nor Ashidi Mat Isa

Authors
  1. Onn Ting Chong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wei Hong Lim
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nor Ashidi Mat Isa
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Koon Meng Ang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sew Sun Tiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Chun Kit Ang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wei Hong Lim .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chong, O.T., Lim, W.H., Isa, N.A.M., Ang, K.M., Tiang, S.S., Ang, C.K. (2020). A Teaching-Learning-Based Optimization with Modified Learning Phases for Continuous Optimization. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Computing. SAI 2020. Advances in Intelligent Systems and Computing, vol 1229. Springer, Cham. https://doi.org/10.1007/978-3-030-52246-9_8

Download citation

Publish with us

Policies and ethics