An optimization method for corn planting parameters based on mutation breeding sea horse optimization algorithm

doi:10.1016/j.compag.2025.110417

Return

An optimization method for corn planting parameters based on mutation breeding sea horse optimization algorithm

2025-05-08

PRE

Jinling Bei

Jiquan Wang *

Hongyu Zhang

DOI：10.1016/j.compag.2025.110417

Original

Original request for help

Save

Abstract

En 中文

In response to the black box problem of optimizing corn planting parameters, and considering the shortcomings of traditional methods such as low fitting accuracy and susceptibility to local optima, a MBSHO-BPNN method based on mutation breeding seahorse optimization algorithm (MBSHO) and BP neural network (BPNN) is proposed. Firstly, the standard sea horse optimization algorithm (SHO) and its improved MBSHO are introduced, including control parameter improvement, spiral movement update, and mutation breeding mechanism. Subsequently, a series of extensive numerical experiments are conducted to systematically evaluate the effectiveness of MBSHO components and related parameters. MBSHO is also compared to other algorithms using the CEC 2017 tests on problems of different sizes. The findings indicated that MBSHO demonstrated superior performance. In the effective verification of MBSHO-BPNN, this method outperforms other comparative methods to fit accuracy and optimization results for unconstrained and linearly constrained optimization problems. Ultimately, the MBSHO - BPNN was applied to the optimization of corn planting parameters, and the optimal parameter combination was obtained: the planting density is 9.23 x 104/hm2, nitrogen fertilizer application rate is 138.72 kg/hm2,phosphorus fertilizer application rate is 86.53 kg/hm2, and the potassium fertilizer application rate is 70.32 kg/hm2. Under this configuration, the corn yield reached 16,303.56 kg/hm2, which is significantly higher than that of other methods. The relative error of the actual average yield is only - 0.6757 %. This method not only provides an efficient solution to the agricultural black-box optimization problem but also exhibits potential for broader nonlinear optimization challenges.

Keywords:

Corn yield

Sea horse optimization algorithm

BP neural network

Black-box optimization method