Intelligent Optimization Using Multi-objective Genetic Algorithms in New Media Art Design

This article introduces an advanced optimization algorithm tailored for new media art, aiming to validate the efficacy of multimedia interactive technology and intelligent optimization techniques in computer-aided design (CAD). To accomplish this, we employ both multi-objective genetic algorithms (MOGA) and particle swarm optimization (PSO) for processing and analyzing datasets comprising new media art. Comparative experiments reveal that MOGA outperforms PSO in terms of classification accuracy, mean absolute error (MAE), and recall rate, demonstrating its superior reliability. These findings underscore MOGA's proficiency in handling multimedia resource data analysis and offer more robust optimization support for CAD in the realm of new media art. By integrating the unique attributes of new media art with audience preferences, our algorithm enhances the interactive multimedia effects of artworks, delivering a more intelligent and personalized interactive experience. Looking ahead, we are committed to exploring further applications of optimization algorithms in new media art to propel the continued evolution of multimedia interactive technology. © 2024 U-turn Press LLC.