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. However, the algorithm suffers from
premature convergence, slow convergence rate and large computational time for optimizing the computationally expensive
objective functions. Therefore, an attempt to speed up TLBO is considered necessary. This paper introduces a modification
to basic TLBO that enhances the convergence rate without compromising with the solution quality. The performance
of modified TLBO (mTLBO) on a test of functions is compared with original TLBO and other popular evolutionary
techniques such as Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), Differential evolution (DE) etc.
It is found that mTLBO requires less computational efforts to locate global optimal solution. Further, the proposed
mTLBO is implemented for few well known benchmark data clustering problems and its performances compared with
basic TLBO as well. Results reveal that mTLBO is able to effectively cluster data points with better cluster performance
measures such as quantization errors, intra cluster and inter cluster distances compared to TLBO. This paper examines six
patents on optimization using evolutionary approach and their applications to different engineering fields.