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Y. Shi and R. C. Eberhart, “Parameter Selection in Particle Swarm Optimization,” Lecture Notes in Computer Science Evolutionary Programming VII, Vol. 1447, 1998, pp. 591-600. doi:10.1007/BFb0040810

has been cited by the following article:

• 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.