A Hybrid PSO-GA Model for Optimizing Crop Planting Strategies

Abstract

In the context of rural revitalization, optimizing crop planting strategies is essential for promoting sustainable agricultural development and enhancing rural economic benefits. This paper proposes a novel multi-objective optimization model that hybridizes Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) to overcome the individual limitations of these widely used algorithms. The model uniquely integrates multiple practical constraints—including crop types, yield per unit area, market demand, selling price, planting costs, and soil suitability—into a comprehensive framework aimed at maximizing overall economic returns. By effectively combining PSO’s fast convergence with GA’s robust global search ability, the hybrid algorithm enhances convergence stability and solution accuracy in complex, high-dimensional agricultural planning problems. Furthermore, this study introduces a tailored constraint-handling mechanism to better reflect real-world agricultural scenarios. Extensive experiments on actual agricultural data demonstrate that the proposed model outperforms traditional single-algorithm approaches in both efficiency and effectiveness, providing reliable decision support for optimizing crop planting strategies. The innovative fusion of PSO and GA, coupled with practical constraint integration, distinguishes this research and contributes significantly to the advancement of intelligent agricultural decision-making under rural revitalization initiatives.