Research on Industrial Structure Dynamic Evolution Based on Multi-objective Optimization and Complex Networks

Abstract

In the context of economic globalization, the interdependencies among industries have become increasingly complex, and traditional linear analysis methods are insufficient to meet the demands of optimizing industrial structures while balancing multiple objectives, such as employment and environmental sustainability. This study develops an innovative model that integrates dynamic systems, complex network theory, and multi-objective optimization techniques to analyze and optimize the industrial structure. The model accounts for the evolution of industrial interactions over time and seeks to maximize GDP, employment, and sustainability. Experimental results show that the optimal investment allocation across industries can significantly enhance GDP, with key sectors such as Finance and Technology contributing the most to the economic output. The model's dynamic evolution highlights differences in industry growth trajectories, and the analysis suggests that Finance, Manufacturing, and Energy are central to economic development. Visualizations reveal that optimal investment allocation can lead to a 15% increase in overall GDP while improving employment by 12% and reducing environmental costs by 8%. These findings offer valuable insights for policy-making and strategic investments aimed at promoting sustainable economic growth.