Design of Crop Planting Strategies in the Mountainous Areas of North China Based on a Multi-Objective Optimization Model
DOI:
https://doi.org/10.54097/ps1gyw98Keywords:
Multi-objective optimization, NSGA-II algorithm, MOEA/D algorithm.Abstract
With the development of the organic farming industry, reasonable crop planting strategies are critical to improving agricultural benefits. Taking a village in the mountainous areas of North China as an example, this paper proposes the optimal crop planting plans from 2024 to 2030 to achieve sustainable rural economic development. Based on data, a multi-objective optimization model is established, considering objectives such as maximizing total profit and minimizing planting area dispersion, and incorporating constraints such as suitable plots for crops and avoiding continuous cropping of the same crop. The NSGA-II algorithm is used to solve the multi-objective optimization model, generating multiple Pareto-optimal solutions and reducing excessive planting. For surplus production, the MOEA/D algorithm is adopted to handle multi-objective optimization problems. The model calculates a maximum profit of 46,895,620 CNY and determines the optimal planting strategy. After considering surplus production, the maximum profit increases to 48,123,540 CNY. The crop planting plan proposed in this paper balances the relationship between profit and planting area dispersion and optimizes adjustments for surplus production to maximize village economic benefits. The optimal crop planting strategies for 2024 to 2030 demonstrate that the multi-objective optimization model can effectively address the problem of optimization design in crop planting strategies and provide a scientific basis for improving agricultural benefits. This study also provides a reference for designing crop planting strategies in other regions.
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[1] WANG Liufang, WANG Lixiang, FU Zengguang, et al. Research on nitrogen fixation and after-effects of annual legumes[J]. Journal of Northwest Agriculture and Forestry University (Natural Science Edition),1986,(04):34-49.
[2] Wang Xiaohui, Cheng Jiali, Zhang Jiaen, Chen Fu. Influences of crop diversification on yield, resource use efficiency, and environmental footprint in farmland landscapes in intensive farming [J]. Science of the Total Environment, 2024-10-01.
[3] Sugapriya C, Nagarajan D, Gobinath V M, et al. A multi-period optimization model for medicine supply chains using modified interactive multi-objective fuzzy programming[J]. Supply Chain Analytics, 2023, 4: 100048.
[4] Duan P, Yu Z, Gao K, et al. Solving the multi-objective path planning problem for mobile robot using an improved NSGA-II algorithm[J]. Swarm and Evolutionary Computation, 2024, 87: 101576.
[5] Zhang T, Li F, Zhao X, et al. A convolutional neural network-based surrogate model for multi-objective optimization evolutionary algorithm based on decomposition[J]. Swarm and Evolutionary Computation, 2022, 72: 101081.
[6] Cheng G, Ying S, Wang B. Tuning configuration of apache spark on public clouds by combining multi-objective optimization and performance prediction model[J]. Journal of Systems and Software, 2021, 180: 111028.
[7] González-Santos C, Vega-Rodríguez M A, Pérez C J. Addressing topic modeling with a multi-objective optimization approach based on swarm intelligence[J]. Knowledge-Based Systems, 2021, 225: 107113.
[8] Wang Y J, Wang G G, Tian F M, et al. Solving energy-efficient fuzzy hybrid flow-shop scheduling problem at a variable machine speed using an extended NSGA-II[J]. Engineering Applications of Artificial Intelligence, 2023, 121: 105977.
[9] Rahimbakhsh H, Kohansal M E, Tarkashvand A, et al. Multi-objective optimization of natural surveillance and privacy in early design stages utilizing NSGA-II[J]. Automation in Construction, 2022, 143: 104547.
[10] Yang Z, Qiu H, Gao L, et al. Surrogate-assisted MOEA/D for expensive constrained multi-objective optimization[J]. Information Sciences, 2023, 639: 119016.
[11] Wang Q, Gu Q, Chen L, et al. A MOEA/D with global and local cooperative optimization for complicated bi-objective optimization problems[J]. Applied Soft Computing, 2023, 137: 110162.
[12] Wang W, Dai S, Zhao W, et al. Multi-objective optimization of hexahedral pyramid crash box using MOEA/D-DAE algorithm[J]. Applied Soft Computing, 2022, 118: 108481.
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