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A modified teaching–learning-based optimization algorithm for numerical function optimization

International Journal of Machine Learning and Cybernetics volume 10, pages 1357–1371 (2019)Cite this article

Abstract

In this paper, a kind of modified teaching–learning-based optimization algorithm (MTLBO) is proposed to enhance the solution quality and accelerate the convergence speed of the conventional TLBO. Compared with TLBO, the MTLBO algorithm possesses different updating mechanisms of the individual solution. In teacher phase of the MTLBO, the students are divided into two groups according to the mean result of learners in all subjects. Moreover, the two groups present different updating strategies of the solution. In learner phase, the students are still divided into two groups, where the first group includes the top half of the students and the second group contains the remaining students. The first group members increase their knowledge through interaction among themselves and study independently. The second group members increase their marks relying on their teacher. According to the above-mentioned updating mechanisms, the MTLBO can provide a good balance between the exploratory and exploitative capabilities. Performance of the proposed MTLBO algorithm is evaluated by 23 unconstrained numerical functions and 28 CEC2017 benchmark functions. Compared with TLBO and other several state-of-the-art optimization algorithms, the results indicate that the MTLBO shows better solution quality and faster convergence speed.

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References

  1. Rao RV, Savsani VJ, Vakharia DP (2012) Teaching–learning-based optimization: an optimization method for continuous non-linear large scale problems. Inf Sci 183(1):1–15

    MathSciNet  Article  Google Scholar 

  2. Doğan B, Ölmez T (2015) A new metaheuristic for numerical function optimization: vortex search algorithm. Inf Sci 293:125–145

    Article  Google Scholar 

  3. Kennedy J. Particle swarm optimization[M]//encyclopedia of machine learning. Springer US, 2010: 760–766

  4. Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39(3):459–471

    MathSciNet  Article  MATH  Google Scholar 

  5. Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248

    Article  MATH  Google Scholar 

  6. Gandomi AH, Alavi AH (2012) Krill herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simul 17(12):4831–4845

    MathSciNet  Article  MATH  Google Scholar 

  7. Cuevas E, Cienfuegos M, Zaldívar D et al (2013) A swarm optimization algorithm inspired in the behavior of the social-spider. Expert Syst Appl 40(16):6374–6384

    Article  Google Scholar 

  8. Mirjalili SSCA. (2016) A Sine Cosine Algorithm for solving optimization problems. Knowl Based Syst 96:120–133

    Article  Google Scholar 

  9. Rao RV, Savsani VJ, Vakharia DP (2011) Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43(3):303–315

    Article  Google Scholar 

  10. Li G, Niu P, Xiao X (2012) Development and investigation of efficient artificial bee colony algorithm for numerical function optimization. Appl Soft Comput 12(1):320–332

    Article  Google Scholar 

  11. Mernik M, Liu SH, Karaboga D et al (2015) On clarifying misconceptions when comparing variants of the Artificial Bee Colony Algorithm by offering a new implementation. Inf Sci 291:115–127

    MathSciNet  Article  MATH  Google Scholar 

  12. Petrović M, Vuković N, Mitić M et al (2016) Integration of process planning and scheduling using chaotic particle swarm optimization algorithm. Expert Syst Appl 64:569–588

    Article  Google Scholar 

  13. Mandal B, Roy PK, Mandal S (2014) Economic load dispatch using krill herd algorithm. Int J Electr Power Energy Syst 57:1–10

    Article  Google Scholar 

  14. Niu P, Ma Y, Li M et al (2016) A kind of parameters self-adjusting extreme learning machine. Neural Process Lett 44(3):813–830

    Article  Google Scholar 

  15. Ghasemi M, Ghavidel S, Rahmani S et al (2014) A novel hybrid algorithm of imperialist competitive algorithm and teaching learning algorithm for optimal power flow problem with non-smooth cost functions. Eng Appl Artif Intell 29:54–69

    Article  Google Scholar 

  16. Niknam T, Fard AK, Baziar A (2012) Multi-objective stochastic distribution feeder reconfiguration problem considering hydrogen and thermal energy production by fuel cell power plants. Energy 42(1):563–573

    Article  Google Scholar 

  17. Niknam T, Azizipanah-Abarghooee R, Narimani MR (2012) An efficient scenario-based stochastic programming framework for multi-objective optimal micro-grid operation. Appl Energy 99:455–470

    Article  Google Scholar 

  18. Niknam T, Azizipanah-Abarghooee R, Narimani MR (2012) A new multi objective optimization approach based on TLBO for location of automatic voltage regulators in distribution systems. Eng Appl Artif Intell 25(8):1577–1588

    Article  Google Scholar 

  19. Niknam T, Golestaneh F, Sadeghi MS (2012) θ-Multiobjective teaching–learning-based optimization for dynamic economic emission dispatch. Syst J IEEE 6(2):341–352

    Article  Google Scholar 

  20. Rao RV, Kalyankar VD (2013) Parameter optimization of modern machining processes using teaching–learning-based optimization algorithm. Eng Appl Artif Intell 26(1):524–531

    Article  Google Scholar 

  21. Rao RV, Kalyankar VD (2012) Multi-objective multi-parameter optimization of the industrial LBW process using a new optimization algorithm. Proc Inst Mech Eng Part B J Eng Manuf 226(6):1018–1025

    Article  Google Scholar 

  22. Krishnanand KR, Panigrahi BK, Rout PK et al (2011) Application of multi-objective teaching–learning-based algorithm to an economic load dispatch problem with incommensurable objectives[M]//swarm, evolutionary, and memetic computing. Springer, Berlin, pp 697–705

    Google Scholar 

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

    Article  Google Scholar 

  24. Rao R, Patel V (2013) Comparative performance of an elitist teaching–learning-based optimization algorithm for solving unconstrained optimization problems. Int J Ind Eng Comput 4(1):29–50

    Google Scholar 

  25. Rao RV, Patel V (2013) An improved teaching–learning-based optimization algorithm for solving unconstrained optimization problems. Scientia Iranica 20(3):710–720

    Google Scholar 

  26. Roy PK, Bhui S (2013) Multi-objective quasi-oppositional teaching learning based optimization for economic emission load dispatch problem. Int J Electr Power Energy Syst 53:937–948

    Article  Google Scholar 

  27. Črepinšek M, Liu SH, Mernik M (2013) Exploration and exploitation in evolutionary algorithms: a survey. ACM Comput Surv 45(3):35

    MATH  Google Scholar 

  28. Awad NH, Ali MZ, Liang JJ, Qu BY, Suganthan PN (2016) Problem definitions and evaluation criteria for the CEC2017 special session and competition on single objective bound constrained real-parameter numerical optimization, Technical Report, Nanyang Technological University, Singapore

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Acknowledgements

The authors make a grateful acknowledgement for the associate editor and the reviewers for their valuable time and thoughtful comments to the improvement of the paper. This work is supported by the National Natural Science Foundation of China (Grant no. 61573306).

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Authors and Affiliations

  1. School of Electrical Engineering, Yanshan University, Qinhuangdao, 066004, Hebei, China

    Peifeng Niu & Yunpeng Ma

  2. Hydropower Station of Administration of Taolinkou Reservoir, Qinhuangdao, 066004, Hebei, China

    Shanshan Yan

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  1. Peifeng Niu
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  2. Yunpeng Ma
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  3. Shanshan Yan
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Correspondence to Yunpeng Ma.

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Niu, P., Ma, Y. & Yan, S. A modified teaching–learning-based optimization algorithm for numerical function optimization. Int. J. Mach. Learn. & Cyber. 10, 1357–1371 (2019). https://doi.org/10.1007/s13042-018-0815-8

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  • DOI: https://doi.org/10.1007/s13042-018-0815-8

Keywords

  • Teaching–learning-based optimization
  • Modified teaching–learning-based optimization
  • Exploratory and exploitative capabilities
  • Unconstrained numerical functions
  • CEC2017