Weighted Teaching-Learning-Based Optimization for Global Function Optimization

Affiliation(s)

Anil Neerukonda Institute of Technology and Sciences, Vishakapatnam, India.

Majhighariani Institute of Technology & Science, Rayagada, India.

Centurion University of Technology and Management, Paralakhemundi, India.

Anil Neerukonda Institute of Technology and Sciences, Vishakapatnam, India.

Majhighariani Institute of Technology & Science, Rayagada, India.

Centurion University of Technology and Management, Paralakhemundi, India.

Abstract

Teaching-Learning-Based Optimization (TLBO) is recently being used as a new, reliable, accurate and robust optimization technique scheme for global optimization over continuous spaces [1]. This paper presents an, improved version of TLBO algorithm, called the Weighted Teaching-Learning-Based Optimization (WTLBO). This algorithm uses a parameter in TLBO algorithm to increase convergence rate. Performance comparisons of the proposed method are provided against the original TLBO and some other very popular and powerful evolutionary algorithms. The weighted TLBO (WTLBO) algorithm on several benchmark optimization problems shows a marked improvement in performance over the traditional TLBO and other algorithms as well.

Cite this paper

S. Satapathy, A. Naik and K. Parvathi, "Weighted Teaching-Learning-Based Optimization for Global Function Optimization,"*Applied Mathematics*, Vol. 4 No. 3, 2013, pp. 429-439. doi: 10.4236/am.2013.43064.

S. Satapathy, A. Naik and K. Parvathi, "Weighted Teaching-Learning-Based Optimization for Global Function Optimization,"

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