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

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.

[1]  M Aswatha Kumar,et al.  Proceedings of International Conference on Advances in Computing , 2012 .

[2]  Anima Naik,et al.  Rough set and teaching learning based optimization technique for optimal features selection , 2013, Central European Journal of Computer Science.

[3]  Anima Naik,et al.  Data Clustering Based on Teaching-Learning-Based Optimization , 2011, SEMCCO.

[4]  Jun Zhang,et al.  Adaptive Particle Swarm Optimization , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[5]  Amit Konar,et al.  Differential Evolution Using a Neighborhood-Based Mutation Operator , 2009, IEEE Transactions on Evolutionary Computation.

[6]  R. Venkata Rao,et al.  Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems , 2011, Comput. Aided Des..

[7]  S. Satapathy,et al.  High dimensional real parameter optimization with teaching learning based optimization , 2012 .

[8]  Anima Naik,et al.  Improvement of Initial Cluster Center of C-means using Teaching Learning based Optimization☆ , 2012 .

[9]  Reinhard Männer,et al.  Parallel Problem Solving from Nature — PPSN III , 1994, Lecture Notes in Computer Science.

[10]  W. G. Bickley,et al.  Relaxation Methods in Theoretical Physics , 1947 .

[11]  Bijaya K. Panigrahi,et al.  Application of Multi-Objective Teaching-Learning-Based Algorithm to an Economic Load Dispatch Problem with Incommensurable Objectives , 2011, SEMCCO.

[12]  Sanyang Liu,et al.  Improved artificial bee colony algorithm for global optimization , 2011 .

[13]  Anima Naik,et al.  QoS Multicast Routing Using Teaching Learning Based Optimization , 2013 .

[14]  Eisenhart Churchill,et al.  Selected techniques of statistical analysis for Scientific and Industrial Research and Production and Management Engineering , 1948 .

[15]  Sam Kwong,et al.  Gbest-guided artificial bee colony algorithm for numerical function optimization , 2010, Appl. Math. Comput..

[16]  Vedat Toğan,et al.  Design of planar steel frames using Teaching–Learning Based Optimization , 2012 .

[17]  Vivek Patel,et al.  An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems , 2012 .

[18]  R. Venkata Rao,et al.  Mechanical Design Optimization Using Advanced Optimization Techniques , 2012 .

[19]  R. Venkata Rao,et al.  Multi-objective optimization of combined Brayton and inverse Brayton cycles using advanced optimization algorithms , 2012 .

[20]  R. Venkata Rao,et al.  Teaching-Learning-Based Optimization: An optimization method for continuous non-linear large scale problems , 2012, Inf. Sci..

[21]  Anima Naik,et al.  Weighted Teaching-Learning-Based Optimization for Global Function Optimization , 2013 .

[22]  Bilal Alatas,et al.  Chaotic bee colony algorithms for global numerical optimization , 2010, Expert Syst. Appl..

[23]  Anima Naik,et al.  0-1 Integer Programming for Generation Maintenance Scheduling in Power Systems Based on Teaching Learning Based Optimization (TLBO) , 2012, IC3.

[24]  Junjie Li,et al.  Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions , 2011, Inf. Sci..

[25]  Arthur C. Sanderson,et al.  JADE: Adaptive Differential Evolution With Optional External Archive , 2009, IEEE Transactions on Evolutionary Computation.

[26]  Swagatam Das,et al.  Automatic Clustering Using an Improved Differential Evolution Algorithm , 2007 .

[27]  Kenneth A. De Jong,et al.  A Cooperative Coevolutionary Approach to Function Optimization , 1994, PPSN.

[28]  R. Venkata Rao,et al.  Parameter Optimization of Machining Processes Using a New Optimization Algorithm , 2012 .

[29]  Saman K. Halgamuge,et al.  Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients , 2004, IEEE Transactions on Evolutionary Computation.