## 1 Introduction
The particle swarm optimization (PSO) algorithm was adopted as an optimization strategy for determining the optimum parameters [11]. The PSO algorithm mimics the concept of the social interaction of birds in a flock. To perform the multi-objective optimization, the cuckoo optimization algorithm (COA) was used with the objective function, which was derived from the adaptive neuro-fuzzy inference system (ANFIS) model [12]. The COA was developed based on the obligate brood parasitism of some cuckoo species. A multi-objective Java (MO-Jaya) algorithm was implemented in the ECM process to obtain multiple optimal solutions [13]. The differential evolution (DE) algorithm has been used for both the single- and multi-objective optimization of process parameters [14]. [PERSON] and [PERSON] (2017) considered the ECM process to evaluate the performance of the black hole algorithm (BHA). The performance measures of the MRR and overcut (OC) were taken to obtain the optimum machining parameters using the BHA [15]. The harmony search (HS) algorithm was combined with the DF so that an HS-DF optimizer was proposed to optimize the ECM process parameters [16]. [PERSON] and [PERSON] conducted a comparative analysis with meta-heuristics such as the firefly algorithm (FA), DE algorithm, ant colony optimization (ACO) algorithm, and teaching-learning-based optimization (TLBO) algorithm for the optimization of various control parameters of an ECM process [17]. Apart from these meta-heuristics, artificial neural networks (ANNs) and fuzzy logic (FL) were also employed to model the ECM experiments and subsequent optimization process [18; 19]. Several optimization algorithms have been reported in the literature, and their efficiency was tested with many benchmark functions and real-time applications. The grey wolf optimizer (GWO), a recently developed meta-heuristic, was proposed by [PERSON] et.al [20]. The GWO is inspired by the hunting behavior of the grey wolves in nature, and it ensures a better trade-off between the exploration and exploitation abilities of the algorithm. It was applied in many engineering applications, emphasizing its efficiency [21]. The significant characteristic of this algorithm is that it does not converge toward some local optima and helps store the best possible solutions obtained so far by its social hierarchy nature [22]. [PERSON] and [PERSON] implemented GWO to minimize milling performances (i.e., MRR, cutting force, and surface roughness) and proved the application potential in a manufacturing environment [23]. [PERSON] and [PERSON] optimized the wire electrical discharge machining (WEDM) parameters with the GWO algorithm [24]. [PERSON] and [PERSON] experimented with the efficiency of the GWO algorithm for parametric optimization of the abrasive water jet machining (AWJM) process and found it to be successful [25]. A performance comparison of the response surface methodology (RSM), genetic algorithm (GA), and GWO algorithm was conducted for prediction of the surface roughness in ball-end milling of hardened steel, and GWO was found to be superior [26]. The grey relational analysis (GRA)-based GWO was implemented for milling experiments to determine the machining conditions of the spindle speed, feed rate, and depth of cut for the optimum surface roughness, cutting force, and MRR [27]. [PERSON] proposed another meta-heuristic, the moth-flame optimization (MFO) algorithm, which was inspired by the navigation method of moths in nature [28]. It gained attention for solving engineering optimization problems immediately. The improved MFO algorithm was proposed by [PERSON] et al. to maintain a balance between global and local searching [29]. A milling optimization problem was solved to emphasize its effectiveness in solving manufacturing problems [30]. The MFO algorithm was implemented to identify the optimal set of turning parameters to minimize the machinability indices.