Research Article ABiologicalImmuneMechanism-BasedQuantumPSOAlgorithm and Its Application in Back Analysis for Seepage Parameters Jiacheng Tan, Liqun Xu , Kailai Zhang, and Chao Yang College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China Correspondence should be addressed to Liqun Xu; xuliqun6.2@163.com Received 23 November 2019; Revised 11 May 2020; Accepted 12 May 2020; Published 22 June 2020 Academic Editor: Haiyan Lu Copyright © 2020 Jiacheng Tan et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Back analysis for seepage parameters is a classic issue in hydraulic engineering seepage calculations. Considering the charac- teristics of inversion problems, including high dimensionality, numerous local optimal values, poor convergence performance, and excessive calculation time, a biological immune mechanism-based quantum particle swarm optimization (IQPSO) algorithm was proposed to solve the inversion problem. By introducing a concentration regulation strategy to improve the population diversity and a vaccination strategy to accelerate the convergence rate, the modified algorithm overcame the shortcomings of traditional PSO which can easily fall into a local optimum. Furthermore, a simple multicore parallel computation strategy was applied to reduce computation time. e effectiveness and practicability of IQPSO were evaluated by numerical experiments. In this paper, taking one concrete face rock-fill dam (CFRD) as a case, a back analysis for seepage parameters was accomplished by utilizing the proposed optimization algorithm and the steady seepage field of the dam was analysed by the finite element method (FEM). Compared with immune PSO and quantum PSO, the proposed algorithm had better global search ability, convergence performance, and calculation rate. e optimized back analysis could obtain the permeability coefficient of CFRD with high accuracy. 1. Introduction In hydraulic engineering, a seepage calculation is used to obtain hydraulic factors such as head, discharge, and gra- dient by utilizing the basic seepage parameters for seepage stability analysis and operation management [1]. Deter- mining correct seepage parameters is an important issue in seepage calculation, which directly influences the accuracy and rationality of seepage field analysis. Generally, there are three methods to determine seepage parameters including the empirical formula method, testing method, and back analysis method. Based on mathematical assumptions and empirical estimations, the results of the empirical formula method are inaccurate and not suitable in complex struc- tures. e testing method, including indoor and in situ tests, can acquire accurate permeability coefficients. However, when the number of samples is small, the results are in- accurate for whole structures, and then, the number of samples is large and the cost is excessive. e back analysis method, based on the measured data and numerical sim- ulation results, can cost-effectively obtain rational seepage parameters. erefore, it is necessary to develop a back analysis method to supplement and improve the work of determining seepage parameters. e essence of the back analysis method is to acquire material parameters while minimizing the error between the computed and measured values. Considering that the back analysis method requires repeated iterations and numerous calculations, many scholars have recently introduced various optimization algorithms into back analysis to solve the global optimal solution. Some intelligent algorithms, such as genetic algorithm (GA) [2], particle swarm optimization (PSO) [3], support vector machine (SVM) [4, 5], artificial bee colony (ABC) [6], and artificial neural network (ANN) [7–9], have been widely used in back analysis for mechanical parameters. e work in the seepage field has also achieved some progress. For example, based on the concept of “nonlinear mapping,” Chi et al. [10], Ni and Chi [11], and Hindawi Mathematical Problems in Engineering Volume 2020, Article ID 2191079, 13 pages https://doi.org/10.1155/2020/2191079