Engineering Optimization Model for Infrastructure Allocation Using Enhanced Genetic Algorithms

In the domain of complex infrastructure systems engineering, conventional methodologies encounter significant challenges in achieving balanced allocation under multidimensional technical constraints. This study proposes an enhanced genetic algorithm-based optimization model for municipal infrastructure distribution problems. The computational framework integrates multiple operational parameters, capacity metrics, and system performance indicators into a unified engineering model. Quantitative analysis of operational data revealed a 15% enhancement in allocation efficiency across five distribution nodes (918, 1139, 1025, 1284, and 1142 units respectively) and achieved system performance value of 96,362. Comprehensive sensitivity analysis demonstrated that effort ratios between 0.3-0.5 maintained system stability, confirming the algorithm's computational robustness. To extend the engineering application to regions with heterogeneous infrastructure requirements, the study further developed a differential equation model comparing two distinct geographical settings, where optimal allocation values reached 2,889, 3,073, 2,502, 4,919, and 1,121 units, yielding maximum function value of 661,194. The mathematical model was further enhanced through a tripartite optimization system that improved long-term operational stability by 23.5%. This engineering framework provides a generalizable computational solution for infrastructure distribution problems across varying technical conditions.