Research on the Implementation Path of Multi-Objective Optimization Algorithm for English Translation Information Technology Convergence in Big Data

The big data environment is dynamically changing, so the multi-objective optimization algorithm for the integration of English translation information technology needs to have dynamic adaptability. In this paper, we first construct a multi-objective learning parameter model for English translation information technology. Then a reference point-based environment unpredictable dynamic multi-objective optimization algorithm (UDERP) is proposed to realize the dynamic adaptability of the multi-objective optimization algorithm. Finally, the designed English translation information technology incorporating the UDERP algorithm is simulated and tested. The performance of UDERP algorithm, DNSGA-II algorithm and DSS algorithm are compared with each other using three test functions of FDA series. When the environment changes the optimal solution derived from the algorithm proposed in this paper is closer to the real Pareto solution. Comparing the neural machine translation based on cross-language pre-trained language model and the neural machine translation based on multi-coverage model, the English translation information technology designed in this paper has a better convergence effect and can realize more accurate parameter estimation.