With the transformation from traditional manufacturing to intelligent manufacturing, customer-oriented personalized customization has gradually become the main mode of production. Interactive algorithms determine the pros and cons of the solution via customers which can make customers better participants in the customization process. However, if the population size is expanded and the number of evolutionary iterations is too high, frequent interactions are likely to cause customer fatigue. This paper proposes an adaptive interactive artificial immune algorithm based on improved hierarchical clustering. This algorithm uses the improved hierarchical clustering algorithm to optimize generation of the initial antibodies and applies the affinity calculation method based on customer intention, adaptive crossover and mutation operators, and a multisolution reservation method based on hybrid selection strategy to the artificial immune algorithm. Via empirical research on the customized operational data of wheel hubs, the proposed method effectively solves the problem of customer fatigue, significantly improves the convergence speed of the algorithm and reduces the time cost.

随着从传统制造向智能制造的转变，以客户为导向的个性化定制已逐渐成为主要的生产方式。交互式算法通过客户确定解决方案的优缺点，这可以使客户更好地参与定制过程。但是，如果人口规模扩大且进化迭代次数过多，则频繁的交互可能会导致客户疲劳。提出了一种基于改进层次聚类的自适应交互式人工免疫算法。该算法使用改进的层次聚类算法来优化初始抗体的生成，并基于客户意图，自适应交叉和变异算子应用亲和力计算方法，一种基于混合选择策略的人工免疫算法多解决方案预留方法。通过对轮毂定制运行数据的实证研究，提出的方法有效解决了客户疲劳问题，大大提高了算法的收敛速度，降低了时间成本。