Performance of Teaching Learning Based Optimization Algorithm with Various Teaching Factor Values for Solving Optimization Problems

  • M. Ramakrishna Murty
  • J. V. R. Murthy
  • P. V. G. D. Prasad Reddy
  • Anima Naik
  • Suresh Chandra Satapathy
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 247)


Teaching Learning Based Optimization (TLBO) is being used as a new, reliable, accurate and robust optimization technique scheme for global optimization over continuous spaces. This paper presents an effect of variation of a teaching factor TF in traditional TLBO algorithm and then proposed a value for teaching factor TF. The traditional TLBO algorithm with new teaching factor TF value has been tested on several benchmark functions and shown to be statistically significantly better than other teaching factor values for performance measures in terms of faster convergence behavior.


Benchmark Function Teaching Learn Initial Cluster Center Teaching Factor Brayton Cycle 
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.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • M. Ramakrishna Murty
    • 1
  • J. V. R. Murthy
    • 2
  • P. V. G. D. Prasad Reddy
    • 3
  • Anima Naik
    • 4
  • Suresh Chandra Satapathy
    • 5
  1. 1.Dept of CSEGMRITRajamIndia
  2. 2.Dept of CSEJNTUKKakinadaIndia
  3. 3.Dept of CS&SEAndhra UniversityVisakhapatnamIndia
  4. 4.Dept of CSEMITSRayagadaIndia
  5. 5.Dept of CSEANITSVisakhapatnamIndia

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