Optimal Planting Strategy for Crops Based on Multi-objective Optimization Algorithm

Peiyao Li; Dantong Zhang; Yilin Chen

doi:10.54097/wxd2hf94

Authors

Peiyao Li
Dantong Zhang
Yilin Chen

DOI:

https://doi.org/10.54097/wxd2hf94

Keywords:

Multi-objective Optimization, Nondominated Sorting Genetic Algorithm II, Multi-objective Evolutionary Algorithm Based on Decomposition.

Abstract

The sustainable development of rural economy has always been the focus of national and local governments. Under the limited cultivated land resources, the development of organic planting industry according to local conditions can not only improve the utilization rate of land, but also promote the stable growth of rural economy. Based on the data of the American College Students Mathematical Modeling Competition, this paper uses NSGA-II and MOEA/D model to solve the optimal planting scheme and maximum profit of farmers under different sales situations when crop overcapacity in 2024-2030. In addition, this paper also uses Robust NSGA-II algorithm to obtain the optimal planting plan and maximum profit after considering the uncertainty of expected sales volume, yield per mu, planting cost and selling price of crops, which provides theoretical reference for realizing the reasonable allocation of agricultural resources and increasing farmers' income. Finally, this study found that, the best-planting scheme solved by Robust NSGA-II algorithm has higher profit than NSGA-II algorithm and MOEA/D algorithm, while algorithms in uncertain situations are more realistic, indicating that Robust NSGA-II algorithm can guide farmers' crop planting plan.

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