A comparative study on multi-objective pareto optimization of WEDM process using nature-inspired metaheuristic algorithms

Due to its ability to generate complex and intricate shape features on different hard-to-cut materials with minimum dimensional deviation and higher surface finish, wire electrical discharge machining (WEDM) process has already become well-acknowledged in various modern-day manufacturing industries. For optimizing the machining performance of this process, applications of numerous metaheuristic algorithms and multi-criteria decision making (MCDM) techniques are already available in the existent literature. In this paper, six recently developed but yet to be popular nature-inspired metaheuristic algorithms, i.e. multi-objective ant lion optimization (MOALO) algorithm, multi-objective dragonfly algorithm (MODA), multi-objective grasshopper optimization algorithm (MOGOA), multi-objective grey wolf optimizer (MOGWO), non-dominated sorting moth flame optimization (NSMFO) algorithm and non-dominated sorting whale optimization algorithm (NSWOA) are employed for Pareto optimization of a WEDM process. From the developed Pareto optimal fronts, the optimal parametric combinations of the said process are extracted with the help of a post priori MCDM tool called combined compromise solution (CoCoSo) method. It is revealed that for all the considered nature-inspired metaheuristics, the optimal parametric intermixes vary with respect to the weights assigned to the responses. However, MOGWO, MOGOA and MODA are able to identify the best solutions 47%, 28% and 20% of the cases respectively.