A Resource Planning Framework Based on Particle Swarm Algorithm and Goal Planning

With the acceleration of urbanization and the intensification of resource constraints, rural areas in China are confronting significant challenges that necessitate innovative resource planning and optimization models. To tackle these complex issues, this study presents a robust computational framework for crop resource planning that leverages advanced algorithms and optimization techniques. First, we develop a three-dimensional model incorporating key variables for linear planning. The K-means clustering algorithm is utilized to categorize targets based on their specific requirements, providing valuable insights into the substitutability and complementarity of the factors being optimized, which is essential for data-driven decision-making. Building on this foundation, we construct a single-objective linear programming model aimed at maximizing the effectiveness of the optimization search to formulate the optimal strategy. Finally, we employ the Particle Swarm Optimization (PSO) algorithm, a powerful computational method renowned for its efficiency in solving complex optimization problems. The effectiveness of the model in optimizing cropping strategies and enhancing income potential is validated using agricultural data from rural areas in North China. This study not only underscores the importance of computational techniques in agricultural planning but also offers valuable insights for other regions facing similar challenges, thereby advancing the integration of technology in sustainable resource management.