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Collective information-based teaching–learning-based optimization for global optimization

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Abstract

Teaching–learning-based optimization (TLBO) has been widely used to solve global optimization problems. However, the optimization problems in various fields are becoming more and more complex. The canonical TLBO is easy to be trapped in the local optimum when dealing with these problems. In this paper, a new TLBO algorithm with collective intelligence concept introduced is proposed, namely collective information-based TLBO (CIBTLBO). CIBTLBO uses the information from the top learners to form CITeachers and uses the neighborhood information of each learner to form NTeachers, and these teachers help other learners learn in the teacher phase. Furthermore, CITeacher also helps in the learner phase. To demonstrate superiority of the proposed algorithm, experiments on 28 benchmark functions from CEC2013 are carried out, and the benchmark functions are set to 10, 30, 50 and 100 dimensions, respectively. The results show that the proposed CIBTLBO algorithm outperforms the other previous related algorithms.

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References

  • Das S, Abraham A, Chakraborty UK, Konar A (2009) Differential evolution using a neighborhood-based mutation operator. IEEE Trans Evol Comput 13(3):526–553

    Article  Google Scholar 

  • Fogel LJ (1994) Evolutionary programming in perspective: the top-down view. In: Computational intelligence: imitating life. IEEE Press, Piscataway

  • Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–70

    Article  Google Scholar 

  • Ghasemi M, Ghavidel S, Rahmani S, Roosta A, Falah H (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 

  • Ghasemi M, Ghavidel S, Gitizadeh M, Akbari E (2015a) An improved teaching–learning-based optimization algorithm using Lévy mutation strategy for non-smooth optimal power flow. Int J Electr Power Energy Syst 65:375–384

    Article  Google Scholar 

  • Ghasemi M, Taghizadeh M, Ghavidel S, Aghaei J, Abbasian A (2015b) Solving optimal reactive power dispatch problem using a novel teaching–learning-based optimization algorithm. Eng Appl Artif Intell 39:100–108

    Article  Google Scholar 

  • Goldberg DE (1989) Genetic algorithms in search optimization and machine learning. Addison-Wesley, Reading

    MATH  Google Scholar 

  • Han Huang H, Qin ZH, Lim A (2012) Example-based learning particle swarm optimization for continuous optimization. Inf Sci 182(1):125–138

    Article  MathSciNet  MATH  Google Scholar 

  • Heylighen F (1999) Collective intelligence and its implementation on the web: algorithms to develop a collective mental map. Comput Math Organ Theory 5(3):253–280

    Article  MATH  Google Scholar 

  • Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8(1):687–697

    Article  Google Scholar 

  • Kennedy J (1999) Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance. In: Proceedings of the 1999 congress on evolutionary computation-CEC99 (Cat. No. 99TH8406), Washington, DC, vol 3, pp 1938

  • Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural network, pp 1942–1948

  • Lévy P (1997) Collective intelligence. Harper Collins, New York

    Google Scholar 

  • Liang JJ, Qin AK, Suganthan PN, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10(3):281–295

    Article  Google Scholar 

  • Liang JJ, Qu BY, Suganthan PN, Hernández-Díaz AG (2013) Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization, Technical report. Nanyang Technological University

  • Malone TW, Laubacher R, Dellarocas CN (2009) Harnessing crowds: mapping the genome of collective intelligence. MIT Sloan Research Paper 4732-09

  • McGonigal J (2008) Why i love bees: a case study in collective intelligence gaming. In: Salen K (ed) The ecology of games: connecting youth, games, and learning. MIT Press, Cambridge, MA, pp 199–227

    Google Scholar 

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

    Article  Google Scholar 

  • Niknam T, Azizipanah-Abarghooee R, Aghaei J (2013) A new modified teaching–learning algorithm for reserve constrained dynamic economic dispatch. IEEE Trans Power Syst 28(2):749–763

    Article  Google Scholar 

  • Patel V, Savsani V (2016) Multi-objective optimization of a stirling heat engine using TS-TLBO (tutorial training and self learning inspired teaching–learning based optimization) algorithm. Energy 95:528–541

    Article  Google Scholar 

  • Peram T, Veeramachaneni K, Mohan CK (2003) Fitness-distance-ratio based particle swarm optimization. In: Swarm intelligence symposium, SIS ‘03. Proceedings of the 2003 IEEE, pp 174–181

  • Qu BY, Suganthan PN, Das S (2013) A distance-based locally informed particle swarm model for multimodal optimization. IEEE Trans Evol Comput 17(3):387–402

    Article  Google Scholar 

  • Raja BD, Jhala RL, Patel V (2016) Multi-objective optimization of a rotary regenerator using tutorial training and self-learning inspired teaching–learning based optimization algorithm (TS-TLBO). Appl Therm Eng 93:456–467

    Article  Google Scholar 

  • Rao RV (2015) Teaching–learning-based optimization algorithm and its engineering applications. Springer, London

    Google Scholar 

  • Rao RV, Patel V (2012) An elitist teaching–learning-based optimization algorithm for solving complex constrained optimization problems. Int J Ind Eng Comput 3(4):535–560

    Google Scholar 

  • 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 

  • Rao RV, Patel V (2014) A multi-objective improved teaching–learning based optimization algorithm for unconstrained and constrained optimization problems. Int J Ind Eng Comput 5(1):1–22

    Google Scholar 

  • 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 

  • 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

    Article  MathSciNet  Google Scholar 

  • Sheskin D (2003) Handbook of parametric and nonparametric statistical procedures. Chapman & Hall, London

    Book  MATH  Google Scholar 

  • Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702–713

    Article  Google Scholar 

  • Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341–359

    Article  MathSciNet  MATH  Google Scholar 

  • Venkata Rao R, Patel V (2013) Multi-objective optimization of heat exchangers using a modified teaching–learning-based optimization algorithm. Appl Math Model 37(3):1147–1162

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  • Weber M, Neri F, Tirronen V (2011) A study on scale factor in distributed differential evolution. Inf Sci 181(12):2488–2511

    Article  Google Scholar 

  • Wolpert DH, Tumer K (1999) An introduction to collective intelligence. arXiv preprint cs/9908014

  • Xu Y, Wang L, Wang S, Liu M (2015) An effective teaching–learning-based optimization algorithm for the flexible job-shop scheduling problem with fuzzy processing time. Neurocomputing 148:260–268

    Article  Google Scholar 

  • Zheng LM, Zhang SX, Tang KS, Zheng SY (2017) Differential evolution powered by collective information. Inf Sci 399:13–29

    Article  Google Scholar 

  • Zou F, Wang L, Hei XH, Chen DB, Yang DD (2014a) Teaching–learning-based optimization with dynamic group strategy for global optimization. Inf Sci 273(8):112–131

    Article  Google Scholar 

  • Zou F, Wang L, Hei X, Chen D, Jiang Q, Li H (2014b) Bare-bones teaching–learning-based optimization. Sci World J 2014(4):1–17

    Google Scholar 

Download references

Funding

This study was funded by the National Natural Science Foundation of China (No. 61671485) and Guangdong Natural Science Foundation (No. 2015A030312010).

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Authors and Affiliations

  1. School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, 510006, China

    Zi Kang Peng, Shao Yong Zheng & Yun Liang Long

  2. Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong, China

    Sheng Xin Zhang

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  1. Zi Kang Peng
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  2. Sheng Xin Zhang
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  3. Shao Yong Zheng
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  4. Yun Liang Long
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Correspondence to Shao Yong Zheng.

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Communicated by V. Loia.

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Peng, Z.K., Zhang, S.X., Zheng, S.Y. et al. Collective information-based teaching–learning-based optimization for global optimization. Soft Comput 23, 11851–11866 (2019). https://doi.org/10.1007/s00500-018-03741-2

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