Performance Optimization of Web Front-End Frameworks: Automatic Adjustment Strategies Based on Bayesian Optimization Algorithm
Cuiqin Chen, Qing Meng, Junze Huang · 2024
In modern web development, performance optimization of front-end frameworks has become a key issue in improving user experience and system efficiency. The existing manual adjustment methods are often time-consuming and have unstable effects. This article proposes an automatic adjustment strategy based on Bayesian optimization algorithm to achieve efficient optimization of front-end framework performance. After using Bayesian optimization algorithm, the average time of Largest Contentful Paint (LCP) is reduced to 2052 milliseconds. In terms of response speed indicators, the optimized time to interactive (TTI) decreases to 2923 milliseconds. Users are also very satisfied with the optimized experience. After using Bayesian optimization algorithm, the CPU (Central Processing Unit) utilization rate decreases to 65.9% and the memory usage decreases to 324.4MB. From the data conclusion, it can be seen that the automatic adjustment strategy based on Bayesian optimization algorithm has shown significant advantages in improving the performance, response speed, user experience, and resource utilization efficiency of web front-end frameworks.