Discrete Teaching Learning-Based Optimization for Multi-Hole Drilling

Abstract

In manufacturing industries drilling multi-hole is one of the major operations. Hence, the drill path sequence optimization has attracted considerable attention to reduce the processing cost. Consequently, researchers have proposed the use of newly modified algorithms, hybridized as well as classical standalone evolutionary algorithms. In this study, a recently developed Discrete Teaching-Learning-Based Optimization (D-TLBO) in the domain of the TSP is proposed for optimizing the multi-hole drill path sequencing problems. To examine the performance of the D-TLBO two multi-hole drill test problems with 14 and 158 holes are used and the results are compared with the best available solutions from the literature. A D-TLBO algorithm has shown the ability to efficiently search for the optimal solution in a discrete space and a comparative study also confirms that the algorithm performs better than existing evolutionary algorithms.