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  4. Vol 9, Issue 4, 2025

Improvement of Particle Swarm Algorithm for Multi-objective Planting Scheme Optimization and Empirical Analysis

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DOI: 10.23977/acss.2025.090412 | Downloads: 1 | Views: 62

Author(s)

Yanzhuo Wu 1, Guangwu Ao 1

Affiliation(s)

1 School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051, China

Corresponding Author

Guangwu Ao

ABSTRACT

To address the issues of low resource utilization efficiency and poor multi-objective coordination in traditional planting scheme optimization, by integrating the particle swarm algorithm with machine learning technology, an improved particle swarm algorithm that incorporates adaptive inertia weight, chaotic disturbance mechanism, and Pareto elite retention strategy is proposed. A multi-objective planting scheme optimization model is constructed. Taking a typical agricultural area as the empirical object, multi-dimensional data such as soil, climate, and market are collected. The decision variables such as crop types, planting area, and irrigation strategy are optimized through the improved algorithm. The experimental results show that the convergence speed of the improved algorithm is 32.6% and 21.8% higher than that of the standard PSO and MOPSO algorithms respectively. The optimal planting scheme generated can increase the total crop yield of the region by 15.3%, improve the water resource utilization rate by 28.5%, and increase the economic benefits by 19.7%. This research verifies the effectiveness and superiority of the improved particle swarm algorithm in multi-objective planting optimization, providing a scientific basis and technical support for agricultural modernization planting decisions.

KEYWORDS

Improvement of particle swarm algorithm; Multi-objective optimization; Planting plan; Machine learning; Empirical analysis

CITE THIS PAPER

Yanzhuo Wu, Guangwu Ao, Improvement of Particle Swarm Algorithm for Multi-objective Planting Scheme Optimization and Empirical Analysis. Advances in Computer, Signals and Systems (2025) Vol. 9: 98-107. DOI: http://dx.doi.org/10.23977/acss.2025.090412.

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