Development and Applications of a New Optimization Algorithm

  • R. Venkata Rao
  • Vimal J. Savsani
Part of the Springer Series in Advanced Manufacturing book series (SSAM)


All the nature-inspired algorithms such as genetic algorithm (GA), PSO, BBO, ABC, DE, etc. require algorithm parameters to be set for their proper working. Proper selection of parameters is essential for the searching of the optimum solution by these algorithms. A change in the algorithm parameters changes the effectiveness of the algorithms. To avoid this difficulty, an optimization method named ‘Teaching–Learning-Based Optimization (TLBO)’ is presented in this chapter. This method works on the effect of influence of a teacher on learners. The performance of the proposed TLBO method is checked with the recent and well-known optimization algorithms such as GA, ABC, PSO, HS, DE and hybrid algorithms by experimenting with different constrained and unconstrained benchmark problems and mechanical element design optimization problems with different characteristics. The effectiveness of TLBO method is also checked for different performance criteria, like success rate, mean solution, average function evaluations required, convergence rate, etc. The results show better performance of TLBO method over other natured-inspired optimization methods for the considered benchmark functions and mechanical element design optimization problems. Also, the TLBO method shows better performance with less computational effort for the large-scale problems, i.e. problems with high dimensions.


Particle Swarm Optimization Differential Evolution Benchmark Problem Harmony Search Benchmark Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Ahrari A, Atai A (2010) Grenade explosion method-a novel tool for optimization of multimodal functions. Appl Soft Comput 10(4):1132–1140CrossRefGoogle Scholar
  2. 2.
    Shu SFK, Erwie Z (2007) A hybrid simplex search and particle swarm optimization for unconstrained optimization. Eur J Oper Res 181:527–548MATHCrossRefGoogle Scholar
  3. 3.
    Karaboga D, Akay B (2009) Artificial bee colony (ABC), harmony search and bees algorithms on numerical optimization. In: IPROMS-2009, Innovative production machines and systems virtual conference, Cardiff, UKGoogle Scholar
  4. 4.
    Akay B, Karaboga D (2010) Artificial bee colony algorithm for large-scale problems and engineering design optimization. J Intell Manuf. doi:  10.1007/s10845-010-0393-4
  5. 5.
    Liu H, Cai Z, Wang Y (2010) Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization. Appl Soft Comput 10:629–640CrossRefGoogle Scholar
  6. 6.
    Amirjanov A (2006) The development of a changing range genetic algorithm. Comput Methods Appl Mech Eng 195:2495–2508MathSciNetMATHCrossRefGoogle Scholar
  7. 7.
    Tessema B, Yen G (2006) A self adaptive penalty function based algorithm for constrained optimization. Proceedings of 2006 IEEE congress on evolutionary computation, pp 246–253Google Scholar
  8. 8.
    Huang FA, Wang L, He Q (2007) An effective co-evolutionary differential evolution for constrained optimization. Appl Math Comput 186(1):340–356MathSciNetMATHCrossRefGoogle Scholar
  9. 9.
    Becerra RL, Coello CAC (2006) Cultured differential evolution for constrained optimization. Comput Methods Appl Mech Eng 195:4303–4322MATHCrossRefGoogle Scholar
  10. 10.
    Krohling RA, Coelho LS (2006) Coevolutionary particle swarm optimization using Gaussian distribution for solving constrained optimization problems. IEEE Trans Syst Man Cybern Part B Cybern 36(6):1407–1416CrossRefGoogle Scholar
  11. 11.
    Montes E, Coello CAC (2005) A simple multi-membered evolution strategy to solve constrained optimization problems. IEEE Trans Evol Comput 9(1):1–17CrossRefGoogle Scholar
  12. 12.
    Parsopoulos K, Vrahatis M (2005) Unified particle swarm optimization for solving constrained engineering optimization problems. In: Proceedings of advances in natural computation, LNCS 3612, Springer-Verlag, Berlin, pp 582–591Google Scholar
  13. 13.
    He Q, Wang L (2007) An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Eng Appl Artif Intell 20:89–99CrossRefGoogle Scholar
  14. 14.
    Suganthan PN, Hansen N, Liang JJ, Deb K, Chen A, Auger YP, Tiwari S (2005) Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. Technical report, Nanyang Technological University, Singapore. <>
  15. 15.
    Deb K (2000) An efficient constraint handling method for genetic algorithms. Comput Methods Appl Mech Eng 186:311–338MATHCrossRefGoogle Scholar
  16. 16.
    Takahama T, Sakai S (2006) Constrained optimization by the constrained differential evolution with gradient-based mutation and feasible elites. Proceedings of IEEE congress on evolutionary computation, Vancouver, BC, Canada, pp 1–8Google Scholar
  17. 17.
    Runarsson TP, Yao X (2005) Search biases in constrained evolutionary optimization. IEEE Trans Syst Man Cybern 35:233–243CrossRefGoogle Scholar
  18. 18.
    Mallipeddi R, Suganthan PN (2010) Ensemble of constraint handling techniques. IEEE Trans Evol Comput 14:561–579CrossRefGoogle Scholar
  19. 19.
    Wang Y, Cai Z, Guo G, Zhou Y (2007) Multiobjective optimization and hybrid evolutionary algorithm to solve constrained optimization problems. IEEE Trans Syst Man Cybern 37:560–575CrossRefGoogle Scholar
  20. 20.
    Rao RV, Savsani VJ, and Vakharia DP (2011b) Teaching–learning-based optimization: an optimization method for continuous non-linear large scale problems. Inf Sci. doi:  10.1016/j.ins.2011.08.006
  21. 21.
    Rao RV, Savsani VJ, Vakharia DP (2011) Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43:303–315CrossRefGoogle Scholar
  22. 22.
    Rao RV, Kalyankar VD (2011) Parameter optimization of machining processes using a new optimization algorithm. Mat Manuf Process (in press)Google Scholar
  23. 23.
    Rao RV, Patel V (2011) Multi-objective optimization of combined Brayton and inverse Brayton cycle using advanced optimization algorithms. Engg Opt (in press)Google Scholar
  24. 24.
    Rao RV, Savsani VJ, Balic J (2012) Teaching-learning-based optimization algorithm for constrained and unconstrained real parameter optimization problems. Engg Opt (in press)Google Scholar
  25. 25.
    Jian MC (2006) Introducing recombination with dynamic linkage discovery to particle swarm optimization. Technical report NCL-TR-2006006, Department of Computer Science, National Chiao Tung University, TaiwanGoogle Scholar
  26. 26.
    Liang JJ, Runarsson TP, Montes EM, Clerc M, Suganthan PN, Coello CAC, Deb K (2006) Problem definitions and evolution criteria for the CEC 2006 special session on constrained real-parameter optimization. Technical report, Nanyang Technological University, Singapore. <>
  27. 27.
    Ballester PJ, Stephenson J, Carter JN, Gallagher K (2005) Real-parameter optimization performance study on the CEC-2005 benchmark with SPC-PNX. In: The 2005 IEEE congress on evolutionary computation, vol 1, pp 498–505Google Scholar
  28. 28.
    Ronkkonen J, Kukkonen S, Price KV (2005) Real-parameter optimization with differential evolution. In: The 2005 IEEE congress on evolutionary computation, vol 1, pp 506–513Google Scholar
  29. 29.
    Qin AK, Suganthan PN (2005) Self-adaptive differential evolution algorithm for numerical optimization. In: The 2005 IEEE congress on evolutionary computation, vol 2, pp 1785–1791Google Scholar
  30. 30.
    Auger A, Hansen N (2005) A restart CMA evolution strategy with increasing population size. In: The 2005 IEEE congress on evolutionary computation, vol 2, pp 1769–1776Google Scholar

Copyright information

© Springer-Verlag London 2012

Authors and Affiliations

  • R. Venkata Rao
    • 1
  • Vimal J. Savsani
    • 2
  1. 1.Mechanical Engineering DepartmentS.V. National Institute of TechnologySuratIndia
  2. 2.Department of Mechanical EngineeringB. H. Gardi College of Engineering and TechnologyRajkotIndia

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