Model Construction and Simulation Research on Optimizing Community Resource Allocation by Genetic Algorithm

Urbanization and rising service demands necessitate efficient community resource allocation, yet traditional methods struggle with complexity and scale. This study proposes a genetic algorithm (GA)-based model to optimize multi-objective resource allocation, integrating factors like utilization efficiency, cost minimization, and fairness. A dynamic fitness function with adaptive weights and elite strategies enhanced search efficiency and solution quality. Simulations compared GA performance against traditional methods (e.g., linear programming) across diverse scenarios. Results showed GA achieved 82–87% resource utilization efficiency, reduced costs by 25–30%. In large-scale cases, GA maintained balanced allocation under dynamic demands, outperforming traditional methods in flexibility and computational stability. The model demonstrates GA’s superiority in balancing efficiency, cost, and fairness, offering a viable tool for urban management. Future work should address computational scalability via parallelization and integration with deep learning. This research advances GA applications in resource optimization and provides actionable insights for policymakers.