We have developed a global optimization program named PGA based on particle swarm optimization algorithm coupled with genetic operators for the structures of atomic clusters. The effectiveness and efficiency of the PGA program can be demonstrated by efficiently obtaining the tetrahedral Au20 and double-ring tubular B20, and identifying the ground state ZrSi 17 - 20 - $$ {\\mathrm{ZrSi}}_{17\\hbox{--} 20}^{-} $$ clusters through the comparison between the simulated and the experimental photoelectron spectra (PESs). Then, the PGA was applied to search for the global minimum structures of Mg n - $$ {\\mathrm{Mg}}_n^{-} $$ (n = 3-30) clusters, new structures have been found for sizes n = 6, 7, 12, 14, and medium-sized 21-30 were first determined. The high consistency between the simulated spectra and the experimental ones once again demonstrates the efficiency of the PGA program. Based on the ground-state structures of these Mg n - $$ {\\mathrm{Mg}}_n^{-} $$ (n = 3-30) clusters, their structural evolution and electronic properties were subsequently explored. The performance on Au20, B20, ZrSi 17 - 20 - $$ {\\mathrm{ZrSi}}_{17\\hbox{--} 20}^{-} $$ , and Mg n - $$ {\\mathrm{Mg}}_n^{-} $$ (n = 3-30) clusters indicates the promising potential of the PGA program for exploring the global minima of other clusters. The code is available for free upon request.

Colon adenocarcinoma (COAD) is a type of colon cancers with a high mortality rate. Its early symptoms are not obvious, and its late stage is accompanied by various complications that seriously endanger patients\' lives. To assist in the early diagnosis of COAD and improve the detection efficiency of COAD, this paper proposes a multi-level threshold image segmentation (MIS) method based on an enhanced particle swarm algorithm for segmenting COAD images. Firstly, this paper proposes a multi-strategy fusion particle swarm optimization algorithm (DRPSO) with a replacement mechanism. The non-linear inertia weight and sine-cosine learning factors in DRPSO help balance the exploration and exploitation phases of the algorithm. The population reorganization strategy incorporating MGO enhances population diversity and effectively prevents the algorithm from stagnating prematurely. The mutation-based final replacement mechanism enhances the algorithm\'s ability to escape local optima and helps the algorithm to obtain highly accurate solutions. In addition, comparison experiments on the CEC2020 and CEC2022 test sets show that DRPSO outperforms other state-of-the-art algorithms in terms of convergence accuracy and speed. Secondly, by combining the non-local mean 2D histogram and 2D Renyi entropy, this paper proposes a DRPSO algorithm based MIS method, which is successfully applied to the segments the COAD pathology image problem. The results of segmentation experiments show that the above method obtains relatively higher quality segmented images with superior performance metrics: PSNR = 23.556, SSIM = 0.825, and FSIM = 0.922. In conclusion, the MIS method based on the DRPSO algorithm shows great potential in assisting COAD diagnosis and in pathology image segmentation.

In recent years, hypertension has become one of the leading causes of illness and death worldwide. Changes in lifestyle among the population have led to an increasing prevalence of hypertension. This study proposes a non-contact blood pressure estimation method that allows patients to conveniently monitor their blood pressure values. By utilizing a webcam to track facial features and the region of interest (ROI) for obtaining forehead images, independent component analysis (ICA) is employed to eliminate artifact signals. Subsequently, physiological parameters are calculated using the principle of optical wave reflection. The Nelder-Mead (NM) simplex method is combined with the particle swarm optimization (PSO) algorithm to optimize the empirical parameters, thus enhancing computational efficiency and accurately determining the optimal solution for blood pressure estimation. The influences of light intensity and camera distance on the experimental results are also discussed. Furthermore, the measurement time is only 10 s. The superior accuracy and efficiency of the proposed methodology are demonstrated by comparing them with those in other published literature.

Over the past decades, the software industry has expanded to include all industries. Since stakeholders tend to use it to get their work done, software houses seek to estimate the cost of the software, which includes calculating the effort, time, and resources required. Although many researchers have worked to estimate it, the prediction accuracy results are still inaccurate and unstable. Estimating it requires a lot of effort. Therefore, there is an urgent need for modern techniques that contribute to cost estimation. This paper seeks to present a model based on deep learning and machine learning techniques by combining convolutional neural networks (CNN) and the particle swarm algorithm (PSO) in the context of time series forecasting, which enables feature extraction and automatic tuning of hyperparameters, which reduces the manual effort of selecting parameters and contributes to fine-tuning. The use of PSO also enhances the robustness and generalization ability of the CNN model and its iterative nature allows for efficient discovery of hyperparameter similarity. The model was trained and tested on 13 different benchmark datasets and evaluated through six metrics: mean absolute error (MAE), mean square error (MSE), mean magnitude relative error (MMRE), root mean square error (RMSE), median magnitude relative error (MdMRE), and prediction accuracy (PRED). Comparative results reveal that the performance of the proposed model is better than other methods for all datasets and evaluation criteria. The results were very promising for predicting software cost estimation.

As a key guarantee and cornerstone of building quality, the importance of deformation prediction for deep foundation pits cannot be ignored. However, the deformation data of deep foundation pits have the characteristics of nonlinearity and instability, which will increase the difficulty of deformation prediction. In response to this characteristic and the difficulty of traditional deformation prediction methods to excavate the correlation between data of different time spans, the advantages of variational mode decomposition (VMD) in processing non-stationary series and a gated cycle unit (GRU) in processing complex time series data are considered. A predictive model combining particle swarm optimization (PSO), variational mode decomposition, and a gated cyclic unit is proposed. Firstly, the VMD optimized by the PSO algorithm was used to decompose the original data and obtain the Internet Message Format (IMF). Secondly, the GRU model optimized by PSO was used to predict each IMF. Finally, the predicted value of each component was summed with equal weight to obtain the final predicted value. The case study results show that the average absolute errors of the PSO-GRU prediction model on the original sequence, EMD decomposition, and VMD decomposition data are 0.502 mm, 0.462 mm, and 0.127 mm, respectively. Compared with the prediction mean square errors of the LSTM, GRU, and PSO-LSTM prediction models, the PSO-GRU on the PTB0 data of VMD decomposition decreased by 62.76%, 75.99%, and 53.14%, respectively. The PTB04 data decreased by 70%, 85.17%, and 69.36%, respectively. In addition, compared to the PSO-LSTM model, it decreased by 8.57% in terms of the model time. When the prediction step size increased from three stages to five stages, the mean errors of the four prediction models on the original data, EMD decomposed data, and VMD decomposed data increased by 28.17%, 3.44%, and 14.24%, respectively. The data decomposed by VMD are more conducive to model prediction and can effectively improve the accuracy of model prediction. An increase in the prediction step size will reduce the accuracy of the deformation prediction. The PSO-VMD-GRU model constructed has the advantages of reliable accuracy and a wide application range, and can effectively guide the construction of foundation pit engineering.

In recent years, swarm intelligence optimization methods have been increasingly applied in many fields such as mechanical design, microgrid scheduling, drone technology, neural network training, and multi-objective optimization. In this paper, a multi-strategy particle swarm optimization hybrid dandelion optimization algorithm (PSODO) is proposed, which is based on the problems of slow optimization speed and being easily susceptible to falling into local extremum in the optimization ability of the dandelion optimization algorithm. This hybrid algorithm makes the whole algorithm more diverse by introducing the strong global search ability of particle swarm optimization and the unique individual update rules of the dandelion algorithm (i.e., rising, falling and landing). The ascending and descending stages of dandelion also help to introduce more changes and explorations into the search space, thus better balancing the global and local search. The experimental results show that compared with other algorithms, the proposed PSODO algorithm greatly improves the global optimal value search ability, convergence speed and optimization speed. The effectiveness and feasibility of the PSODO algorithm are verified by solving 22 benchmark functions and three engineering design problems with different complexities in CEC 2005 and comparing it with other optimization algorithms.

Gold nanohole arrays, hybrid metal/dielectric metasurfaces composed of periodically arranged air holes in a thick gold film, exhibit versatile support for both localized and propagating surface plasmons. Leveraging their capabilities, particularly in surface plasmon resonance-oriented applications, demands precise optical tuning. In this study, a customized particle swarm optimization algorithm, implemented in Ansys Lumerical FDTD, was employed to optically tune gold nanohole arrays treated as bidimensional gratings following the Bragg condition. Both square and triangular array dispositions were considered. Convergence and evolution of the particle swarm optimization algorithm were studied, and a mathematical model was developed to interpret its outcomes.

Vibration displacement is one of the key parameters in fault diagnosis of vibrating screens. Monitoring of acceleration signals of vibrating screens can be disturbed due to various factors such as on-site working conditions and equipment. In order to obtain accurate displacement signals of vibrating screen, the method for converting vibration acceleration to displacement based on improved Savitzky-Golay (S-G) filter is proposed. The Particle Swarm Optimization (PSO) algorithm is used to optimize the window length of the S-G filter with the fixed polynomial. The filters are cascaded to denoise the signals multiple times. The reasonable regularization parameter of the Smoothed Prior Approach (SPA) is calculated to remove the trend item from the acceleration signals. The vibration displacement is obtained by integrating the preprocessed acceleration data in the frequency domain. The results demonstrate that the objectivity of parameter selection of filter is improved, and the denoising effect is significant. The filtering effect of the filter is further improved after cascading. It becomes better as the number of stages of cascade increases. The vibration displacement can be obtained accurately by the proposed method. The vibration test platform is built to verify the correctness of the method.

This paper develops a concentration retrieval technique based on the particle swarm optimization (PSO) algorithm, which is used for a calibration-free wavelength modulation spectroscopy system. As compared with the commonly used technique based on the Levenberg-Marquardt (LM) algorithm, the PSO-based method is less dependent on the pre-characterization of the laser tuning parameters. We analyzed the key parameters affecting the performance of the PSO-based technique and determined their optimal parameter values through testing. Furthermore, we conducted a comparative analysis of the efficacy of two techniques in detecting C2H2 concentration. The results showed that the PSO-based concentration retrieval technique is about 63 times faster than the LM-based one in achieving the same accuracy. Within 5 s, the PSO-based technique can produce findings that are generally consistent with the values anticipated.

Dynamic vibration absorbers (DVAs) are extensively used in the prevention of building and bridge vibrations, as well as in vehicle suspension and other fields, due to their excellent damping performance. The reliable optimization of DVA parameters is key to improve their performance. In this paper, an H∞ optimization problem of a novel three-element-type DVA model including an inerter device and a grounded negative stiffness spring is studied by combining a traditional theory and an intelligent algorithm. Firstly, to ensure the system\'s stability, the specific analytical expressions of the optimal tuning frequency ratio, stiffness ratio, and approximate damping ratio with regard to the mass ratio and inerter-mass ratio are determined through fixed-point theory, which provides an iterative range for algorithm optimization. Secondly, the particle swarm optimization (PSO) algorithm is used to further optimize the four parameters of DVA simultaneously. The effects of the traditional fixed-point theory and the intelligent PSO algorithm are comprehensively compared and analyzed. The results verify that the effect of the coupling of the traditional theory and the intelligent algorithm is better than that of fixed-point theory alone and can make the two resonance peaks on the amplitude-frequency response curves almost equal, which is difficult to achieve using fixed-point theory alone. Finally, we compare the proposed model with other DVA models under harmonic and random excitation. By comparing the amplitude-frequency curves, stroke lengths, mean square responses, time history diagrams, variances and decrease ratios, it is clear that the established DVA has a good vibration absorption effect. The research results provide theoretical and algorithm support for designing more effective DVA models of the same type in engineering applications.