Single phase multi-group teaching learning algorithm for computationally expensive numerical optimization (CEC 2016)

In this work, a Single Phase Multi-Group Teaching Learning Optimization strategy is proposed which is a variant of the Teaching Learning Based Optimization algorithm. The proposed strategy has been used to solve the fifteen single objective, bound constrained, computationally expensive benchmark functions that have been provided as part of one of the competition in IEEE Congress on Evolutionary Computation 2016. The proposed strategy has been designed specifically to handle computationally intensive problems and hence provides competitive results and has relatively low time complexity.