# Non-Cutting Moving Toolpath Optimization with Elitist Non-Dominated Sorting Genetic Algorithm-II
## 1 Introduction  
CNC machine tools are generally preferred for the manufacturing of very complex industrial products in automotive, aerospace, defense, and molding industries to improve efficiency and mass productivity [1]. Computer-aided design (CAD) is responsible for the design phase. On the other hand, computer-aided manufacturing (CAM) manages the manufacturing and production processes and the computer-controlled machinery, automating various aspects of the manufacturing process. These CAD/CAM tools are complementary in the manufacturing process, each serving its distinct purpose [2]. The current CAM technology usually relies on geometric computations for toolpath generation, which often leads to the deviation of the generated toolpaths from the optimal perspective of manufacturing engineering. This software is utilized to program toolpaths and generate the necessary G-code to operate CNC machines. Production engineers can choose one of the standard toolpath methods (zig-zag, concentric, radial, etc.) available in machining sector [3]. Nevertheless, conventional toolpaths, which are developed based solely on geometric calculations without considering the mechanics of the processes, may not be optimal for machining all types of parts. Consequently, when compared to optimized paths, the cutting magnitudes become larger, increase the risk of tool damage, and can cause surface problems. It is crucial to optimize the toolpath in machining to achieve extended tool life and reduce the possibility of tool breakage. Additionally, optimizing the toolpath helps limit tool deflection and minimize surface problems on the machined part [4]. The complex geometries and parts can be manufactured thanks to the advances in CAM systems. Any manual control in CAM software can cause the generation of both productive and non-productive toolpaths based on workpiece geometry [5]. This produced toolpath is subsequently transformed into G-code through CAM software to be executed by CNC machines [6]. Optimized software is crucial in manufacturing as it enhances automation, resulting in time savings, error reduction, and improved precision and productivity throughout the manufacturing process [7]. Machine tools need various and complex control methods for their airtime, spindle speed, feed rate, depth of cut, path length, target materials, cutting processes, and so on. This can cause several issues such as the surface quality, tool lifetime, machining time, etc. [8]. These control processes require a considerable amount of energy, and a tool's power consumption changes dynamically during the process [9; 10]. A critical topic in the literature is the prediction of the energy usage of CNC machine tools for optimal power consumption. This issue can be effectively utilized for monitoring, analyzing, and improving their usage according to their purpose [11]. Path planning is another active research area in the literature. Researchers working on the PP matter have been focused on geometric constraints, distance, cycle time, cutting conditions, part materials, jerk-limited feed rates, and collision avoidance [12]. Studies in this field have typically been conducted with a focus on single-factor improvements. With the active and precise application of intelligent technologies such as evolutionary algorithms (EA) and artificial neural networks (ANN) in manufacturing, the overall usability of the system is intended to be improved, leading to more favorable outcomes such as performance optimization and other positive attributes [13; 14]. Numerous studies have explored the optimization of toolpaths in EAs, primarily using methods such as genetic algorithm (GA), particle swarm optimization (PSO), and artificial immune systems (AISs), etc. [13]. For instance, [PERSON] et al. [15] suggested employing GAs to determine optimal path and location planning for workpieces, aiming to reduce the time needed for robots to process these items.