In this work an enhanced virus-evolutionary genetic algorithm has been developed and applied to optimize a three-objective optimization problem related to an important additive manufacturing/rapid prototyping operation known as selective laser sintering (SLS). Selective laser sintering is a manufacturing process where laser is used as the power source to sinter powdered materials such as metals, superalloys, or even nylon and polyamides. The laser source is collectively applied to points in space determined by a solid model, and further binds the material to fabricate solid components. A response surface experiment was established to study the effect of crucial SLS process parameters on the optimization objective of density, hardness, and tensile strength. With reference to the experimental results, a statistical analysis was conducted to further obtain regression models with high coefficient of determination so that the objectives can be reliably predicted. The models were considered as the objective functions for simultaneously maximizing all three objectives and study the trade-off among them. To compare the results and show that the proposed virus-evolutionary genetic algorithm is prominent, two other population-based heuristics were applied to the same problem, namely multi-objective Greywolf algorithm (MOGWO) and multi-verse optimization algorithm (MOMVO). To evaluate the algorithms and judge superiority with reference to the non-dominated solution sets obtained, the hypervolume indicator as well as statistical results were considered for examination. It was shown that the proposed virus-evolutionary genetic algorithm can advantageously maximize all three objectives simultaneously and constitute a beneficial optimization module with strong potentials to optimize not only the SLS problem formulated in this work, but general engineering optimization problems with quite promising and practically viable outcomes.

在这项工作中，已开发出一种增强的病毒进化遗传算法，并将其应用于优化与重要的增材制造/快速原型制造操作（称为选择性激光烧结（SLS））相关的三目标优化问题。选择性激光烧结是一种制造过程，其中激光被用作动力来烧结粉末状材料，例如金属，高温合金，甚至尼龙和聚酰胺。激光源共同应用于由实体模型确定的空间点，并进一步绑定材料以制造实体组件。建立了响应表面实验，以研究关键SLS工艺参数对密度，硬度和拉伸强度优化目标的影响。参考实验结果，进行统计分析以进一步获得具有高确定系数的回归模型，以便可以可靠地预测目标。这些模型被认为是同时最大化所有三个目标并研究它们之间的取舍的目标函数。为了比较结果并表明所提出的病毒进化遗传算法是突出的，将另外两种基于人群的启发式方法应用于同一问题，即多目标Greywolf算法（MOGWO）和多诗词优化算法（MOMVO）。为了评估算法并参考获得的非支配解集判断优劣，考虑了超量指标以及统计结果。