论文部分内容阅读
针对车辆部件的结构设计制造优化问题,提出了基于Taguchi方法的混合鲁棒人工蜂群算法(HRABC)。采用Taguchi方法生成了目标函数的方差分析(ANOVA)表,根据ANOVA表寻找到设计变量的合理区间,并根据这些区间定义人工蜂群算法的鲁棒初始种群;利用基于目标函数的多个设计变量的影响提取人工蜂群算法的解空间,从而得到优化结果。在车辆部件结构设计优化及多刀具铣削优化问题上,验证了所提算法的有效性及鲁棒性。分析结果表明,与几种常用的优化算法相比,在收敛速度和有效性方面,该HRABC算法具有最好的优化效果。
Aiming at the structural design and manufacturing optimization of vehicle components, a hybrid robust artificial bee colony algorithm (HRABC) based on Taguchi method is proposed. The Taguchi method was used to generate the ANOVA table of objective functions. According to the ANOVA table, reasonable intervals of design variables were found, and robust initial population of artificial bee colony algorithm was defined according to these intervals. Using multiple design variables based on objective function The extraction of artificial bee colony algorithm solution space, resulting in optimization results. In the optimization of vehicle structural design and multi-tool milling optimization, the proposed algorithm is validated and robust. The analysis results show that HRABC algorithm has the best optimization effect compared with several commonly used optimization algorithms in terms of convergence speed and validity.