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Improved Particle Swarm Optimization for Selection of Shield Tunneling Parameter Values

Gongyu Hou1, Zhedong Xu1,*, Xin Liu1, Cong Jin1

Department of Mechanics and Civil Engineering, China University of Mining & Technology, Beijing, 100083, China.

* Corresponding Author: Zhedong Xu. Email: email .

Computer Modeling in Engineering & Sciences 2019, 118(2), 317-337. https://doi.org/10.31614/cmes.2019.04693

Abstract

This article proposes an exponential adjustment inertia weight immune particle swarm optimization (EAIW-IPSO) to enhance the accuracy and reliability regarding the selection of shield tunneling parameter values. According to the iteration changes and the range of inertia weight in particle swarm optimization algorithm (PSO), the inertia weight is adjusted by the form of exponential function. Meanwhile, the self-regulation mechanism of the immune system is combined with the PSO. 12 benchmark functions and the realistic cases of shield tunneling parameter value selection are utilized to demonstrate the feasibility and accuracy of the proposed EAIW-IPSO algorithm. Comparison with other improved PSO indicates that EAIW-IPSO has better performance to solve unimodal and multimodal optimization problems. When solving the selection of shield tunneling parameter values, EAIW-IPSO can provide more accurate and reliable references for the realistic engineering.

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Cite This Article

APA Style
Hou, G., Xu, Z., Liu, X., Jin, C. (2019). Improved particle swarm optimization for selection of shield tunneling parameter values. Computer Modeling in Engineering & Sciences, 118(2), 317-337. https://doi.org/10.31614/cmes.2019.04693
Vancouver Style
Hou G, Xu Z, Liu X, Jin C. Improved particle swarm optimization for selection of shield tunneling parameter values. Comput Model Eng Sci. 2019;118(2):317-337 https://doi.org/10.31614/cmes.2019.04693
IEEE Style
G. Hou, Z. Xu, X. Liu, and C. Jin "Improved Particle Swarm Optimization for Selection of Shield Tunneling Parameter Values," Comput. Model. Eng. Sci., vol. 118, no. 2, pp. 317-337. 2019. https://doi.org/10.31614/cmes.2019.04693



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