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.
