A short study in formulation of TLBO and its synergized applications with clustering

Real world problems are also classified to multi-objective optimization problems since they are tailored with more than one objective functions for which the optimization is advantageous simultaneously. The best possible outcome among these objective function is being optimized from the set of solutions rather than finding a single solution. One of the most recently originated Teaching-Learning-Based Optimization (TLBO) algorithm in Evolutionary Approaches also addresses such issues. This paper aims to formulate the three transcripts basic, elitist and improved TLBO masterminded by R.V. Rao at a single congregation and investigate their competence and vitality in solving multiple objective functions of clustering techniques when used over real-time datasets. Also, this study annals the contributions made by novel researches in synergizing clustering applications with TLBO.