Comparative Study of Multi-hole Drill Path Optimization using Evolutionary Algorithms

Multi-hole drilling is one of the frequently used manufacturing processes in industries. Further, in mass production, multi-hole drilling path optimization plays a crucial role to remain cost-competitive. Usually, the multihole drilling problems are modeled as traveling salesman problems (TSP), and solving them is quite difficult as they fall in the category of NP-hard problems. Researchers have used various evolutionary optimization algorithms like a Genetic algorithm (GA), Particle swarm optimization (PSO), and Ant colony optimization (ACO) to optimize the multi-hole drill path sequences. Literature reveals that Simulated Annealing (SA) is one of the least used standalone algorithms in multihole drilling path optimization even though it is one of the promising algorithms in the presence of multiple local minima. This paper aims to study the performance of SA and compare it with GA, PSO, and, ACO using multi-hole drill path optimization test problems reported in the literature. The outcome reveals that the SA performs quite well.