Metaheuristic-Based Parametric Optimization of Abrasive Water-Jet Machining Process—A Comparative Analysis

Hybrid machining (HM) processes are an effective means for increasing material removal rate, improving surface integrity, minimizing production time and tool wear. Determination of the optimal parametric settings is an important problem in hybrid machining process. The performance of these processes mainly depends on the optimal choice of its various input parameters which highly affect the responses, like material removal rate and surface roughness. In this paper, seven metaheuristic algorithms in the form of artificial bee colony (ABC), ant colony optimization (ACO), particle swarm optimization (ACO), firefly algorithm (FA), differential evolution (DE), teaching–learning-based optimization (TLBO), and elephant swarm water search algorithm (ESWSA) are employed to determine the optimal parametric settings of abrasive water-jet machining (AWJM) process, while satisfying their given sets of practical machining constraints. It is observed that ESWSA outperforms the others with respect to the derived optimal solution, consistency of the solution and convergence speed.