Quantum Swarm Intelligence and Fuzzy Logic: A Framework for Evaluating English Translation
Pei Hong Yang · International Journal of Advanced Computer Science and Applications · 2025
This study introduces the Quantum Swarm-Driven Fuzzy Evaluation Framework (QSI-Fuzzy) for assessing English translation software across multiple domains and criteria. The principal aim is to develop a scalable, adaptive, and interpretable evaluation framework that optimizes dynamic weight assignments while managing linguistic uncertainties. A major challenge in translation software evaluation lies in ensuring accurate and unbiased assessments of semantic accuracy, fluency, efficiency, and user satisfaction, particularly across diverse domains such as Legal, Medical, and Conversational contexts. To address this, QSI-Fuzzy integrates Quantum Swarm Intelligence (QSI) for dynamic weight optimization with fuzzy logic for handling linguistic uncertainties, ensuring robust and adaptive decision-making. Experimental results demonstrate that QSI-Fuzzy outperforms benchmark algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA), achieving faster convergence (55 iterations on average vs. 120 for SA) and exhibiting greater robustness under noisy conditions (maintaining a performance score of 0.80 at 20% noise, compared to 0.70, 0.68, and 0.65 for GA, PSO, and SA, respectively). These findings confirm that QSI-Fuzzy provides an efficient, scalable, and high-performance solution for translation software evaluation, with broader implications for real-time systems, complex decision-making, and multi-domain optimization challenges.