Research on Substation Project Cost Prediction Based on Sparrow Search Algorithm Optimized BP Neural Network

Xu, Xiaomin; Peng, Luyao; Ji, Zhengsen; Zheng, Shipeng; Tian, Zhigang; Geng, Shiping

doi:10.3390/su132413746

Cited by 25 publications

(14 citation statements)

References 13 publications

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“…SSA was inspired by the foraging behavior and anti-predation behavior of sparrows ( 51 ) and has been applied to solve many complex engineering optimization problems ( 52 – 55 ). A brief description of SSA is as follows.…”

Section: Methodsmentioning

confidence: 99%

“…It can simulate the information transmission mode of human brain neurons, perform non-linear transformation and regression processing on complex information variables, and obtain operation results with a high fitting degree. The structure of the BP neural network consists of three layers: input layer, hidden layer, and output layer ( 52 ). The number of hidden layers is not fixed, it can be one layer or multiple layers.…”

Section: Methodsmentioning

confidence: 99%

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Hybridizing five neural-metaheuristic paradigms to predict the pillar stress in bord and pillar method

Zhou

Chen

et al. 2023

Front. Public Health

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Pillar stability is an important condition for safe work in room-and-pillar mines. The instability of pillars will lead to large-scale collapse hazards, and the accurate estimation of induced stresses at different positions in the pillar is helpful for pillar design and guaranteeing pillar stability. There are many modeling methods to design pillars and evaluate their stability, including empirical and numerical method. However, empirical methods are difficult to be applied to places other than the original environmental characteristics, and numerical methods often simplify the boundary conditions and material properties, which cannot guarantee the stability of the design. Currently, machine learning (ML) algorithms have been successfully applied to pillar stability assessment with higher accuracy. Thus, the study adopted a back-propagation neural network (BPNN) and five elements including the sparrow search algorithm (SSA), gray wolf optimizer (GWO), butterfly optimization algorithm (BOA), tunicate swarm algorithm (TSA), and multi-verse optimizer (MVO). Combining metaheuristic algorithms, five hybrid models were developed to predict the induced stress within the pillar. The weight and threshold of the BPNN model are optimized by metaheuristic algorithms, in which the mean absolute error (MAE) is utilized as the fitness function. A database containing 149 data samples was established, where the input variables were the angle of goafline (A), depth of the working coal seam (H), specific gravity (G), distance of the point from the center of the pillar (C), and distance of the point from goafline (D), and the output variable was the induced stress. Furthermore, the predictive performance of the proposed model is evaluated by five metrics, namely coefficient of determination (R2), root mean squared error (RMSE), variance accounted for (VAF), mean absolute error (MAE), and mean absolute percentage error (MAPE). The results showed that the five hybrid models developed have good prediction performance, especially the GWO-BPNN model performed the best (Training set: R2 = 0.9991, RMSE = 0.1535, VAF = 99.91, MAE = 0.0884, MAPE = 0.6107; Test set: R2 = 0.9983, RMSE = 0.1783, VAF = 99.83, MAE = 0.1230, MAPE = 0.9253).

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Hybridizing five neural-metaheuristic paradigms to predict the pillar stress in bord and pillar method

Zhou

Chen

et al. 2023

Front. Public Health

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“…In Xu et al [ 97 ], the SSA algorithm was used to optimize the BP Neural Network parameters to improve the prediction accuracy of the power gride engineering cost. The results showed that the proposed model SSA-BP was a practical solution for such a problem.…”

Section: Recent Variants Of Ssamentioning

confidence: 99%

Recent Versions and Applications of Sparrow Search Algorithm

Awadallah

Al-Betar

Doush

et al. 2023

Arch Computat Methods Eng

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This paper reviews the latest versions and applications of sparrow search algorithm (SSA). It is a recent swarm-based algorithm proposed in 2020 rapidly grew due to its simple and optimistic features. SSA is inspired by the sparrow living style of foraging and the anti-predation behavior of sparrows. Since its establishment, it has been utilized for a plethora of optimization problems in different research topics, such as mechanical engineering, electrical engineering, civil engineering, power systems, industrial engineering, image processing, networking, environment, robotics, planing and scheduling, and healthcare. Initially, the growth of SSA and its theoretical features are highlighted in terms of the number of published articles, citations, topics covered, etc. After that, the different extended versions of SSA are reviewed, where the main variations of SSA are produced to avoid premature convergence and to boost the diversity aspects. These extended versions are modifications and hybridization summarized with more focus on the motivations behind establishing these versions. Multi-objective SSA is also presented as another version to deal with Multi-objective optimization problems. The critical analysis of the main research gaps in the convergence behaviour of SSA is discussed. Finally, the conclusion and the possible future expansions are recommended based on the research works accomplished in the literature.

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“…eoretically, the links between the layers of the network structure are fully linked and the neurons of each layer can also be connected to each other [8][9][10][11]. erefore, combined with experience, DNN is selected.…”

Section: Dnnmentioning

confidence: 99%

Engineering Cost Prediction Model Based on DNN

Bingxin

Xin

Zhang

2022

Scientific Programming

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A DNN-based cost prediction method is proposed for the difficult problem of cost calculation in engineering cost accounting, combined with deep neural networks. Firstly, we introduce the basic information of artificial neural network and select the DNN structure to calculate the engineering cost price according to the characteristics of the data related to engineering cost price. Secondly, the DNN-based engineering cost price prediction model is constructed, and the two types of index systems, engineering characteristics and list item characteristics, are used as model inputs. In addition, the total quotation and each subitem engineering quotation and tax are used as model outputs by analyzing previous relevant studies. Based on this, simulation experiments are conducted on the DNN-based engineering cost price prediction model, and it is concluded from the training model that the DNN model has a better prediction effect. Among them, the relative error of total price forecast by DNN is 4.203%, and the relative error of integrated unit prices V1 and V2 is 2.98% and 4.52%, respectively, with small relative error. Finally, by reasonably adjusting the integrated unit price, the cost price of the integrated unit price and the cost price of the total offer can be calculated.

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Research on Substation Project Cost Prediction Based on Sparrow Search Algorithm Optimized BP Neural Network

Cited by 25 publications

References 13 publications

Hybridizing five neural-metaheuristic paradigms to predict the pillar stress in bord and pillar method

Hybridizing five neural-metaheuristic paradigms to predict the pillar stress in bord and pillar method

Recent Versions and Applications of Sparrow Search Algorithm

Engineering Cost Prediction Model Based on DNN

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