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

Abstract

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

ZHU Xu-hui, SHEN Guo-jiao, XIA Ping-fan, NI Zhi-wei. Model Based on Spirally Evolution Glowworm Swarm Optimization and Back Propagation Neural Network and Its Application in PPP Financing Risk Prediction[J].Computer Science, 2022, 49(6A): 667-674.

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