Application of Multi-Objective Optimization Algorithms for Yak Population Structure Optimization in Alpine Grasslands

The study addresses issues arising from the imbalanced distribution of population structure in yak farming on high-altitude grasslands, including overgrazing, environmental degradation, and decreased income for herders. A multi-objective optimization approach for yak population structure optimization is proposed. The yak population structure optimization problem is modeled as a mathematical framework that considers both pasture resources and environmental impacts. The model integrates multiple factors, including the changes in the age structure of the yak population, the economic benefits of the pasture, and the ecological consequences. To solve this complex multi-objective optimization problem, an improved version of the second-generation Non-Dominated Sorting Genetic Algorithm (NSGA-II) is introduced. This algorithm incorporates an adaptive adjustment mechanism, integrates local search methods, and employs an external elite archive, significantly enhancing the overall efficiency of the algorithm. The optimal population structure is obtained under the most optimal economic outcome, providing effective decision support for the sustainable development of highaltitude grassland farming.