Optimizing Agricultural Planting Strategies with Linear Programming and MOPSO

Abstract

The deep implementation of rural revitalization strategy has put forward higher requirements for our agricultural economy and production. However, in reality, the unreasonable allocation of resources and the impact of natural disasters pose challenges to agricultural output. In this paper, a new agricultural planting optimization model combining linear programming and multi-objective particle swarm optimization algorithm is proposed, and the resource allocation and production conditions in the northern mountainous areas are systematically studied from 2024 to 2030. By integrating economic benefits and ecological constraints, the model realizes the maximization of agricultural benefits and the sustainability of resource utilization. Linear programming effectively optimizes planting area and resource allocation, while MOPSO improves the adaptability and robustness of the model through global search. The model introduces dynamic constraints, such as crop rotation and land use restrictions, and innovatively incorporates the soil restoration benefits of legume crops into the optimization framework. Case studies under different market scenarios (slow sales and low sales) show that the model can provide efficient planting strategies, and its practicability and effectiveness are verified. The research results provide theoretical support and practical guidance for precision agriculture and rural revitalization, and suggest that dynamic variables and data-driven technologies should be combined in the future to further enhance the optimization potential and application value of the model.