Fused Deposition modelling (FDM) is one of the most widely used Additive Manufacturing technologies that extrude a melted plastic filament through a heated nozzle in order to build final physical models layer-by-layer. In this research, a virus-evolutionary genetic algorithm (MOVEGA) is developed and implemented to solve a multi-objective optimization problem related to fused deposition modelling. Taguchi approach was first employed for the experimental procedure design and nine test parts were built according to L9 orthogonal array. The examined process parameters were the deposition angle, layer thickness, and infill ratio each one having three levels. Infill pattern was constant to honeycomb selection. Fabrication time of ABS (Acrylonitrile-Butadiene-Styrene) 3D printed specimens was measured during experiments and analyzed by using Analysis of Means (ANOM) and Analysis of Variance (ANOVA) techniques. Shape accuracy was measured by considering the parts’ dimensions in X, Y and Z axes and expressed as the overall error for control. Regression models were developed to use them as objective functions for a group of multi-objective optimization algorithms. Multi-objective Greywolf (MOGWO), and multi-universe (MOMVO) algorithms where also selected for optimizing the FDM problem to compare results. To evaluate the algorithms and judge superiority with reference to the non-dominated solution sets obtained, the hypervolume indicator was adopted. It has been verified that MOVEGA exhibited superiority in its performance for optimizing FDM problems when compared to heuristics such as MOGWO and MOMVO algorithms whilst it has strong potentials to be coupled with “Internet of Things” (IoT) platforms to facilitate the intelligent optimization control referring to a range of resources, consumables and software.

熔融沉积建模（FDM）是最广泛使用的增材制造技术之一，可通过加热的喷嘴挤出熔融的塑料丝，以逐层构建最终的物理模型。在这项研究中，病毒进化遗传算法（MOVEGA）被开发并实现以解决与融合沉积建模有关的多目标优化问题。首先采用Taguchi方法进行实验程序设计，并根据L9正交阵列构建了9个测试零件。所检查的工艺参数是沉积角，层厚度和填充率，每个参数具有三个级别。填充图案对于蜂窝选择是恒定的。在实验过程中测量ABS（丙烯腈-丁二烯-苯乙烯）3D打印样品的制造时间，并使用均值分析（ANOM）和方差分析（ANOVA）技术进行分析。通过考虑零件在X，Y和Z轴上的尺寸来测量形状精度，并表示为控制的总体误差。开发了回归模型，以将它们用作一组多目标优化算法的目标函数。还选择了多目标灰狼（MOGWO）和多宇宙（MOMVO）算法来优化FDM问题以比较结果。为了评估算法并参考获得的非支配解集判断优劣，采用了超量指标。