Spatio-temporal deep learning model for accurate streamflow prediction with multi-source data fusion

Wang, Zhaocai; Xu, Nannan; Bao, Xiaoguang; Wu, Junhao; Cui, Xuefei

doi:10.1016/j.envsoft.2024.106091

Cited by 22 publications

(1 citation statement)

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“…Although these algorithms can get better solutions in solving these problems than traditional methods, they still have lower accuracy and high time complexity. With the increase of problem complexity, the new optimization methods and machine learning algorithms have become hot topics [27][28][29][30][31][32][33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Artificial bee colony optimization algorithm with full dimensional updating strategy and its application

Li,

Deng

2024

Phys. Scr.

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For the low accuracy and slow convergence speed of artificial bee colony(ABC) algorithm in solving complex optimization problems, an improved artificial bee colony(ABC) algorithm based on the new full dimensional updating ABC/best /1 strategy, namely FNABC was proposed in this paper. In the FNABC, for the low efficiency of one-dimensional search, the full dimensional updating search strategy and ABC/best /1 strategy were combined to design a new full dimensional updating ABC/best/1 strategy, which expanded the search space, improved the mining ability and search efficiency. Then, a new evolutionary phase is designed to balance the global search ability and local mining ability to avoid falling into local optimum and improve the convergence accuracy. Finally, the FNABC is compared with eight state-of-the-art ABC variants such as AABC, iqABC, MEABC, ABCVSS, GBABC, DFSABC, MABC-NS, MGABC in solving 12 complex functions. All functions have obtained the best optimal values among 9 algorithms. Additionally, FNABC is applied to solve a real-world train operation adjustment problem. The experiment results indicate that the FNABC has better optimization ability, scalability and robustness. It obtains the ideal train operation adjustment results.

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