Optimising the parameters in AWJT process using a modified multi-objective DE algorithm

This paper presents a novel multi-objective optimisation algorithm for predicting the optimal control parameters of a radial abrasive water jet turning (AWJT) method.The objective is to maximise the material removal rate (MRR) and minimise the surface roughness (Ra) of the turned surface.The control parameters include water pressure, jet feed speed, abrasive flow rate, surface speed, and nozzle tilted angle.The proposed algorithm, called multi-objective amended differential evolution algorithm (MADEA), is a rank-based differential evolution (DE) algorithm that uses non-dominated sorting and crowding distance to select and update the solutions.The performance of MADEA is compared with six state-of-the-art multi-objective evolutionary algorithms on a set of benchmark test problems and the AWJT problem.The results show that MADEA can find better Pareto optimal solutions than the other algorithms.