User Interface (UI) optimization is essential in the digital era to enhance user satisfaction in web environments. Nevertheless, the existing UI optimization models had overlooked the Cross-Responsiveness (CR) assessment, affecting the user interaction efficiency. Consequently, this article proposes a dynamic web UI optimization through CR assessment using Finite Exponential Continuous State Machine (FECSM) and Quokka Nonlinear Difference Swarm Optimization Algorithm (QNDSOA). Initially, the design and user interaction related information is collected as well as pre-processed for min-max normalization. Next, the Human-Computer Interaction (HCI)-based features are extracted, followed by user behaviour pattern grouping. Meanwhile, the CR assessment is done using FECSM. Then, the proposed Bidirectional Gated Luong and Mish Recurrent Unit (BiGLMRU) is used to classify the User eXperience (UX) change type, which is labelled based on the User Interface Change Prediction Index (UICPI). Lastly, a novel QNDSOA is utilized to optimize the UI design with an average fitness of 98.5632%. Feedback monitoring is done after optimal deployment.

翻译：在数字时代，用户界面（UI）优化对于提升网络环境中的用户满意度至关重要。然而，现有UI优化模型忽视了跨设备响应性（CR）评估，影响了用户交互效率。为此，本文提出了一种通过有限指数连续状态机（FECSM）和短尾矮袋鼠非线性差分群优化算法（QNDSOA）进行CR评估的动态Web UI优化方法。首先，收集与设计和用户交互相关的信息，并进行最小-最大归一化预处理。接着，提取基于人机交互（HCI）的特征，并进行用户行为模式分组。同时，使用FECSM完成CR评估。然后，采用提出的双向门控Luong与Mish循环单元（BiGLMRU）对用户体验（UX）变化类型进行分类，该分类基于用户界面变化预测指数（UICPI）进行标注。最后，利用新颖的QNDSOA优化UI设计，平均适应度达到98.5632%。最优部署后进行反馈监控。