改进的蜣螂优化算法及光伏发电功率预测应用

doi:doi:10.19912/j.0254-0096.tynxb.2024-0715

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改进的蜣螂优化算法及光伏发电功率预测应用

更新时间：2025-10-22

- 改进的蜣螂优化算法及光伏发电功率预测应用
- Vol. 46, Issue 9, Pages: 445-454(2025)
- 作者机构：
- 作者简介：
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- DOI：doi:10.19912/j.0254-0096.tynxb.2024-0715
  CLC： TP301
- Published Online：14 October 2025，
  
  Published：2025
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[J]. 2025, 46(9): 445-454.
DOI：

[J]. 2025, 46(9): 445-454. DOI： doi:10.19912/j.0254-0096.tynxb.2024-0715.

摘要

针对蜣螂优化算法因陷入局部最优值而收敛精度低的问题

提出融合遗传算法改进的蜣螂优化算法

即遗传-蜣螂优化算法（GADBO）

同时结合初始化种群和变异扰动的策略

改善局部最优

提高全局搜索能力。通过10个基准函数的测试和对比

验证改进GADBO算法的有效性

且GADBO比其他群智能优化算法寻优精度更高。GADBO算法应用于长短期记忆神经网络（LSTM）超参数优化

并建立光伏发电功率预测模型。仿真结果表明

以GADBO算法优化而建立的LSTM预测模型拟合系数为98.45%

平均绝对误差和平均绝对百分比误差也最小

模型的准确度和精度都得到提高

验证GADBO在神经网络优化上的适用性和效果。

Abstract

A genetic algorithm-dung beetle optimization （GADBO） is proposed to address the problem of low convergence accuracy of the dung beetle optimization algorithm due to being trapped in local optima. This improved algorithm combines the strategies of initial population and mutation disturbance to escape local optima and enhance global search capabilities. Through the testing and comparison of 10 benchmark functions

the effectiveness of the GADBO algorithm improvement has been verified

and GADBO has higher optimization accuracy than other swarm intelligence optimization algorithms. The GADBO algorithm is applied to the hyperparameter optimization of long short-term memory （LSTM） neural networks

and a photovoltaic power generation prediction model is established. The simulation results show that the LSTM prediction model optimized by the GADBO algorithm has a fitting coefficient of 98.45%

and the average absolute error and average absolute percentage error are also minimized. The accuracy and precision of the model have been improved

verifying the applicability and effectiveness of GADBO in neural network optimization.

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references

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