IMPROVED DUNG BEETLE OPTIMIZATION ALGORITHM AND APPLICATION TO PHOTOVOLTAIC POWER GENERATION PREDICTION

Chen Qingming; Liao Hongfei; Sun Yingkai; Liang Qifeng

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

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (9) : 445-454. DOI: 10.19912/j.0254-0096.tynxb.2024-0715

IMPROVED DUNG BEETLE OPTIMIZATION ALGORITHM AND APPLICATION TO PHOTOVOLTAIC POWER GENERATION PREDICTION

Chen Qingming¹, Liao Hongfei¹, Sun Yingkai², Liang Qifeng¹

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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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genetic algorithms / dung beetle optimization / photovoltaic / power generation / forecasting / long short-term memory

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Chen Qingming, Liao Hongfei, Sun Yingkai, Liang Qifeng. IMPROVED DUNG BEETLE OPTIMIZATION ALGORITHM AND APPLICATION TO PHOTOVOLTAIC POWER GENERATION PREDICTION[J]. Acta Energiae Solaris Sinica. 2025, 46(9): 445-454 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0715

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