Optimizing Multimedia Network Design for English Language Instruction Using an Enhanced Genetic Algorithm Approach

Abstract

The rapid advancement of multimedia technologies and network-based learning environments has significantly transformed English language instruction. However, challenges such as inefficient resource allocation, network latency, lack of personalization, and suboptimal learning outcomes persist. This study proposes an enhanced genetic algorithm (EGA)-based framework for optimizing multimedia network design tailored to English language teaching. The proposed model integrates adaptive mutation, multi-objective optimization, and intelligent resource allocation mechanisms to improve teaching efficiency and learner engagement. Experimental simulations demonstrate that the enhanced genetic algorithm improves system performance by optimizing bandwidth allocation, reducing latency, and enhancing content delivery. Results indicate a 15–20% improvement in system efficiency and a significant increase in learner satisfaction compared to traditional methods. The study contributes to the field of educational technology by providing a scalable and intelligent solution for multimedia network optimization in language education.