Metaheuristics-based parametric optimization of multi-pass turning process: a comparative analysis

In a multi-pass turning process, determination of the optimal values for different machining process parameters has already been identified as a complex optimization problem due to the involvement of numerous real time constraints. In this paper, six metaheuristics, such as artificial bee colony algorithm, ant colony optimization, particle swarm optimization, differential evolution algorithm, firefly algorithm and teaching-learning-based optimization algorithm are implemented to estimate the minimum unit production costs for two different part configurations while fulfilling a given set of machining constraints. It is observed that for both the cases, teaching-learning-based optimization algorithm supersedes the remaining optimization techniques with respect to various predetermined performance measures. Two statistical tests, i.e. paired t test and Wilcoxson signed rank test, also prove the uniqueness of this algorithm as compared to the others.