Enhanced Multi-Strategy Particle Swarm Optimization for Constrained Problems with an Evolutionary-Strategies-Based Unfeasible Local Search Operator

Coarse and Fine Two-Stage Calibration Method for Enhancing the Accuracy of Inverse Finite Element Method

The inverse finite element method (iFEM) is a novel method for reconstructing the full-field displacement of structures by discrete measurement strain. In practical engineering applications, the accuracy of iFEM is reduced due to the positional offset of strain sensors during installation and errors in structural installation. Therefore, a coarse and fine two-stage calibration (CFTSC) method is proposed to enhance the accuracy of the reconstruction of structures. Firstly, the coarse calibration is based on a single-objective particle swarm optimization algorithm (SOPSO) to optimize the displacement-strain transformation matrix related to the sensor position. Secondly, as selecting different training data can affect the training effect of self-constructed fuzzy networks (SCFN), this paper proposes to screen the appropriate training data based on residual analysis. Finally, the experiments of the wing-integrated antenna structure verify the efficiency of the method on the reconstruction accuracy of the structural body displacement field.

A two-stage adaptive penalty method based on co-evolution for constrained evolutionary optimization

Penalty function method is popular for constrained evolutionary optimization. However, it is non-trivial to set a proper penalty factor for a constrained optimization problem. This paper takes advantage of co-evolution to adjust the penalty factor and proposes a two-stage adaptive penalty method. In the co-evolution stage, the population is divided into multiple subpopulations, each of which is associated with a penalty factor. Through the co-evolution of these subpopulations, the performance of penalty factors can be evaluated. Since different penalty factors are used, the subpopulations will evolve along different directions. Thus, exploration can be enhanced. In the shuffle stage, all subpopulations are merged into a population and the best penalty factor from the co-evolution stage is used to guide the evolution. In this manner, the information interaction among subpopulations can be facilitated; thus, exploitation can be promoted. By executing these two stages iteratively, the feasible optimum could be obtained finally. In the two-stage evolutionary process, the search algorithm is designed based on two trial vector generation strategies of differential evolution. Additionally, a restart mechanism is designed to help the population avoid stagnating in the infeasible region. Extensive experiments demonstrate the effectiveness of the proposed method.

A Modified Reptile Search Algorithm for Numerical Optimization Problems

The reptile search algorithm (RSA) is a swarm-based metaheuristic algorithm inspired by the encirclement and hunt mechanisms of crocodiles. Compared with other algorithms, RSA is competitive but still suffers from low population diversity, unbalanced exploitation and exploration, and the tendency to fall into local optima. To overcome these shortcomings, a modified variant of RSA, named MRSA, is proposed in this paper. First, an adaptive chaotic reverse learning strategy is employed to enhance the population diversity. Second, an elite alternative pooling strategy is proposed to balance exploitation and exploration. Finally, a shifted distribution estimation strategy is used to correct the evolutionary direction and improve the algorithm performance. Subsequently, the superiority of MRSA is verified using 23 benchmark functions, IEEE CEC2017 benchmark functions, and robot path planning problems. The Friedman test, the Wilcoxon signed-rank test, and simulation results show that the proposed MRSA outperforms other comparative algorithms in terms of convergence accuracy, convergence speed, and stability.

Artificial Intelligence (AI) Applied in Civil Engineering

In recent years, artificial intelligence (AI) has drawn significant attention with respect to its applications in several scientific fields, varying from big data handling to medical diagnosis [...]

Research on Bridge Damage Identification Based on WPE-MDS and HTF-SAPSO

In recent decades, structural damage identification based on the wavelet analysis method has been widely developed, but it is still confronted with many difficulties, such as large decomposition error and complex data. In order to overcome the shortcomings of analysis based on wavelet, the wavelet packet analysis method is adopted to decompose the acceleration data into wavelet packets, and the frequency band energy value after wavelet packet decomposition (WPE) is taken as the different dimensions of the Mahalanobis distance squared (MDS) in this study, where the MDS value of the same element between different samples is calculated, and the mean value of 30 groups of MDS values for each element is processed. The change rate between the MDS value of the element that exceeds the MDS value in the healthy state and the MDS mean value in the healthy state as the objective function. The combination of weight coefficient and hyperbolic tangent function is used to improve the simulated annealing particle swarm optimization (SAPSO) algorithm, and the improved hyperbolic tangent function-simulated annealing particle swarm optimization (HTF-SAPSO) is used to iteratively calculate the damage severity. The numerical simulation and vibration testing of a steel beam are conducted to verify the identification performance of damage location and the analysis of damage severity by this method, respectively. The numerical model of the experimental I-beam is established based on the MATLAB modeling platform, and the different damage cases are utilized to illustrate the correctness of this study. The different proportions of noise effects are adopted to the numerical simulation analysis, where the correlations between noise effects and MDS value and damage severity are analyzed. In the numerical simulation, although the MDS value increases to different degrees with the increase of the noise ratio, the damage identification result of the damaged element remains mostly constant, which indicates that the influence is negligible. In conclusion, it is feasible to construct the damage index via the combination of WPE and MDS values, the damage location can be judged from whether the MDS value of the element exceeds the threshold, and the HTF-SAPSO algorithm is more efficient and accurate to be adopted in the quantification of the damage severity.

Size and Shape Optimization of a Guyed Mast Structure under Wind, Ice and Seismic Loading

This paper discusses the size and shape optimization of a guyed radio mast for radiocommunications. The considered structure represents a widely industrial solution due to the recent spread of 5G and 6G mobile networks. The guyed radio mast was modeled with the finite element software SAP2000 and optimized through a genetic optimization algorithm (GA). The optimization exploits the open application programming interfaces (OAPI) SAP2000-Matlab. Static and dynamic analyses were carried out to provide realistic design scenarios of the mast structure. The authors considered the action of wind, ice, and seismic loads as variable loads. A parametric study on the most critical design variables includes several optimization scenarios to minimize the structure’s total self-weight by varying the most relevant parameters selected by a preliminary sensitivity analysis. In conclusion, final design considerations are discussed by highlighting the best optimization scenario in terms of the objective function and the number of parameters involved in the analysis.