The main goal of this research is to compare the various optimization strategies (Response Surface Methodology, Taguchi, and Teaching Learning Based Optimization) for orthogonal turning of Hard to Machine materials. The workpiece material in this work is Ti6Al4V alloys. After selecting cutting speeds in the High-Speed Machining range, orthogonal turning tests are performed on the material for a specific combination of machining parameters – Depth of Cut, Cutting Speed, and, Feed Rate. A Lathe Tool Dynamometer is used to record the cutting forces from the trials. After combining Johnson Cook Material and Damage models, a comprehensive Finite Element Model is created to model the Orthogonal Turning of Ti6Al4V alloys. Experiments conducted previously validate the developed model. Three different strategies, namely RSM, Taguchi, and TLBO, were used to optimise machining parameters for minimal Cutting Force. The approaches are compared for the best combination of machining parameters and the best Cutting Force value. Analysis of Variance is used to study the impact of machining factors on Cutting Force.

In the present work, a recently developed advanced optimization algorithm named as teaching–learning-based optimization (TLBO) is used for the parameters optimization of fabric finishing system of a textile industry. Fabric Finishing System has four main subsystems, arranged in hybrid configuration. For performance modeling and analysis of availability, a performance evaluating model of fabric finishing system has been developed with the help of mathematical formulation based on Markov-Birth-Death process using Probabilistic Approach. Then, the overall performance of the concerned system has first analyzed and then, optimized by using teaching–learning-based optimization (TLBO). The results of optimization using the proposed algorithm are validated by comparing with those obtained by using the genetic algorithm (GA) on the same system. Improvement in the results is obtained by the proposed algorithm. The results of effect of variation of the algorithm parameters on fitness values of the objective function are reported.

In this paper, a new optimization technique known as Teaching–Learning-Based Optimization (TLBO) is implemented for solving high dimensional function optimization problems. Even though there are several other approaches to address this issue but the cost of computations are more in handling high dimensional problems. In this work we simulate TLBO for high dimensional benchmark function optimizations and compare its results with very widely used alternate techniques like Differential Evolution (DE) and Particle Swarm Optimization (PSO). Results clearly reveal that TLBO is able to address the computational cost issue for all simulated functions to a large dimensions compared to other two techniques.

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. This paper presents an improved variant of TLBO algorithm, called Improved Teaching–Learning-Based Optimization (ITLBO). A performance comparison of the proposed method is provided against the original TLBO and some other algorithms. The improved TLBO algorithm shows a marked improvement in performance over the traditional TLBO on several benchmark optimization problems.