TITLE:
Weighted Teaching-Learning-Based Optimization for Global Function Optimization
AUTHORS:
Suresh Chandra Satapathy, Anima Naik, K. Parvathi
KEYWORDS:
Function Optimization; TLBO; Evolutionary Computation
JOURNAL NAME:
Applied Mathematics,
Vol.4 No.3,
March
21,
2013
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.