Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (6A): 667-674.doi: 10.11896/jsjkx.210800088

• Interdiscipline & Application • Previous Articles     Next Articles

Model Based on Spirally Evolution Glowworm Swarm Optimization and Back Propagation Neural Network and Its Application in PPP Financing Risk Prediction

ZHU Xu-hui, SHEN Guo-jiao, XIA Ping-fan, NI Zhi-wei   

  1. School of Management,Hefei University of Technology,Hefei 230009,China
    Key Laboratory of Process Optimization and Intelligent Decision-making,Ministry of Education,Hefei 230009,China
  • Online:2022-06-10 Published:2022-06-08
  • About author:ZHU Xu-hui,born in 1991,Ph.D,lectu-rer,master supervisor,is a member of China Computer Federation.His main research interests include evolutionary computing and ensemble learning.
    NI Zhi-wei,born in 1963,Ph.D,professor,Ph.D supervisor.His main research interests include artificial intelligence,machine learning and edge computing.
  • Supported by:
    National Natural Science Foundation of China(91546108,71521001),Natural Science Foundation of Anhui Province(1908085QG298,1908085MG232),Fundamental Research Funds for the Central Universities(JZ2019HGTA0053,JZ2019HGBZ0128),Anhui Provincial Science and Technology Major Projects(201903a05020020) and Open Research Fund Program of Key Laboratory of Process Optimization and Intelligent Decision-making (Hefei University of Technology),Ministry of Education.

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Abstract: Public-private partnership(PPP) projects can improve infrastructure,ensure people's livelihood,and promote the economic development,but there may be a huge loss of capitals and a serious waste of resources among the parties involved because of the characteristics of difficulty in withdrawing funds,long construction cycle and large numbers of participants.Thus,it is important to predict the risks of PPP projects scientifically and accurately.A risk prediction model based on spirally evolution glowworm swarm optimization(SEGSO) and back propagation neural network(BPNN) is proposed in this paper,which is applied for risk prediction in PPP infrastructure projects.Firstly,several strategies such as good point set,communication behavior,elite group and spiral evolution are introduced into the basic GSO,and SEGSO is proposed.Secondly,SEGSO is used to capture better initial weights and thresholds of BPNN to build a SEGSO-BPNN prediction model.Finally,the SEGSO algorithm searching performance is verified on five test functions,and the significance and validity of SEGSO-BPNN model are verified on seven UCI standard datasets.The model is applied to the risk prediction of Chinese PPP projects,and it gains good results,which provides a novel technique for PPP financing risk prediction.

Key words: Back propagation neural network, Glowworm swarm optimization, PPP project, Risk prediction, Spirally evolution

CLC Number: 

  • TP399
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