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In this paper, for the first time, a variable velocity strategy particle swarm optimization (VVS-PSO) is presented to solve the optimization problems ranging from numerical functions to real-world problems. VVS-PSO introduces a new term added in the velocity updating process at each iteration. This new term is controlled by a reduction linear funct...
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In the present research work, an attempt is made to investigate the possible influence of the seismic acceleration on the construction cost of the load-bearing anti-seismic structure of re-inforced concrete (R/C) buildings. It is not uncommon for the design engineer to be confront-ed with the question of dimensioning a building for seismicity great...
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... The inverse identification could be properly addressed by minimizing the objective function using diverse swarm intelligence algorithms, such as genetic algorithm , grey wolf optimizer (Sang-To et al., 2022), particle swarm optimization algorithm (Minh et al., 2023), cuckoo search algorithm (Huang et al., 2019), whale optimization algorithm , Jaya algorithm (Rao, 2016). Among these algorithms, Jaya algorithm receives increasing attention owing to its merits of simple structure and without any algorithm-specific parameters, but it suffers by the problems of slow convergence speed and easy to be trapped into local optimal solution. ...
The external excitations, interface forces and responses at the interface degrees-of-freedom are normally required in many existing substructural condition assessment methods, while they are difficult or even impossible to be accurately measured. To address this issue, a digital twin framework for output-only substructural damage identification with data fusion of muti-type responses is proposed in the present paper. First, heterogeneous responses including displacements, strains and accelerations from the target substructure are measured and divided into two sets. The multi-type responses in measurement set 2 are reconstructed with the first set of responses and transmissibility matrix in time domain. Then, a recovery method is introduced to obtain angular displacements from translational displacements and strains, to acquire angular accelerations from translational accelerations and the second order derivatives of strains by continuous wavelet transform. The recovered angular displacements and angular accelerations are involved into the evaluation of objective function. Besides, to avoid the single and monotonous search operation of traditional optimization algorithms, a reinforced learning-assisted Q-learning hybrid evolutionary algorithm (QHEA) by integrating Q-learning algorithm, differential evolution algorithm, Jaya algorithm, is developed as a search tool to solve the optimization-based inverse problem. The most suitable search strategy among DE/rand/1, DE/rand/2, DE/current-to-best/1, Jaya mutation in each iteration is selected and implemented under the guidance of Q-learning algorithm. Numerical studies on a three-span beam structure are performed to verify the effectiveness of the proposed approach. The results demonstrates that the proposed output-only substructural damage identification approach can accurately identify locations and severities of multiple damages even with high noise-polluted responses.
... A block diagram of the QKD-ERO-MSGCNN is shown in Figure 1. 3.1: Improving the SKR using the VVSPSOA In this section, the VVSPSOA [28] technique is utilized to develop SKRs for collected input signals. The VVSPSOA uses a swarm or population of particles to search for the best possible solution for improving the SKR. ...
6G frameworks will be able to utilize higher frequencies than 5G frameworks and provide altogether higher capacity and much lower latency. First, 6G network input signals stand up to channel obstacles to open security. QKD, a cryptographic technique, allows two parties to safely exchange secret keys for message encryption and decryption. Based on quantum mechanics, any threats in the system warn the authorized users. Qubits can be distributed over fibre or free space quantum channels and encoded with several degrees of freedom. In this proposed method, QKD and Error Rate Optimization using an optimized Multi-Head Self-Attention and Gated-Dilated Convolutional Neural Network (QKD-ERO-MSGCNN) are used to extend the maximum transmission distance and improve the secret key rate (SKR). The variable velocity strategy particle swarm optimization algorithm (VVSPSOA) is used on the input signals to improve the SKR and is fed to the MSGCNN to analyse the quantum bit error rate (QBER) reduction effectively. MSGCNN alone does not optimize the QBER reduction in the channel accurately; intensified sand cat swarm optimization (ISCSO) is used to analyse the QBER reduction accurately. The proposed QKD-ERO-MSGCNN is implemented, and metrics such as accuracy, precision, SKR and QBER reduction attain higher performances when compared with existing techniques.
... where +1 refers to the velocity of particle ( ) at time ( + 1),the symbol refers to the inertia weight which is restricted to a range between (0.4,0,9) and is calculated based on Eq. (3) [28], G and g refers to the total and current generation respectively, 1 and 2 are random values in the range [0, 1], 1 represents the individual (cognitive) learning rate and 2 represents the group (social) learning rate, refers to the current position where denotes the locally best position and refers to the best position of the entire population in the current iteration. Fig. 1 illustrated the framework of the traditional PSO algorithm [29,30]. ...
... In the proposed system, the distribution factor is substantial in the Discovery level, gradually To balance global and local search abilities in the PSO algorithm, the inertia weight ( ) is used. Prior studies often suggested a large value in the early generations, gradually decreasing over time [28,35]. However, this approach may not guarantee optimal performance for all problem types. ...
Cryptography plays a crucial role in safeguarding privacy and confidentiality. Therefore, it is imperative to ensure the security and resistance to cryptanalysis of the employed ciphers. In this paper, an innovative approach called three levels multiple particle swarm optimization (TL-MPSO) has been proposed to enhance the traditional particle swarm optimization (PSO) for cryptanalysis of nonlinear stream ciphers through a ciphertext only attack. We focus on the geffe generator system, which relies on a set of linear feedback shift registers (LFSRs) with a nonlinear combining function. TL-MPSO employs a three-levels approach with distinct search strategies. In the first level, known as the discovery level, particles are randomly distributed into equally sized sub-swarms with no communication between particles in different sub-swarms. The primary objective is to thoroughly explore the search space, with communication limited to neighbouring particles within the same sub-swarm. The second level, called the extraction level, is generated artificially by perturbing the best particles from the discovery level. Particles in different sub-swarms communicate in parallel and co-evolve. Finally, the third level, the optimization level, is artificially generated from the previous level, with all sub-swarms merged into a single large swarm. In all levels, an adaptive movement strategy for each particle is established through a new inertia weight updating process, and a dynamic regrouping strategy is applied to enhance the chances of achieving the global best. Evaluation results demonstrate that the proposed system requires only a minimal number of characters and achieves optimal CPU times. Specifically, when attacking Geffe generators with lengths ranging from 15 to 35, the proposed algorithm consistently achieves optimal solutions with just 10 to 50 cipher characters. Our experiments reveal that TL-MPSO is more accurate and faster than traditional PSO, representing an efficient technique compared to existing cryptanalysis methods.
... Recently, machine learning (ML) has been utilized to measure the strength of structural components, structural responses, failure modes, and the extent of seismic damages. Learning from the data, the ML will be able to determine the practical classification of damages in structural RC components in lieu of immense numerical and experimental analysis [59][60][61][62][63][64][65][66][67][68][69][70]. Several parameters are required for the identification of structural and non-structural damages using ML algorithms. ...
... Various techniques have been proposed for structural damage detection, but the key criteria for an effective method are high precision, ease of implementation, and cost-effectiveness [4][5][6][7][8]. Damage detection methods can be categorized into distinct approaches, including vibration-based [9][10][11], wavelet-based [12][13][14], optimization-based [15][16][17][18][19], machine learning-based [20][21][22][23][24][25]. Several damage detection methods fall under the category of non-destructive testing (NDT) techniques. ...
... Figs. [16][17][18][19] show the first to fourth vibration modes and their corresponding derivatives, respectively. As can be seen, none of these derivatives of the mode shapes can identify the position of the three damages. ...
Detecting structural damages using wavelet transform is effective if a suitable wavelet function is selected. However, the selection of the appropriate wavelet function can be a challenging task. This paper introduces a novel and efficient criterion for selecting the proper wavelet function. It is emphasized that there is a relationship between the accuracy of damage detection using wavelet detail coefficients and the correctness of the approximation of the original function. Accordingly, we define a ratio function and then a novel wavelet selection criterion called WSC in this study. After applying the proposed WSC criterion for 128 wavelet functions, it is found that the wavelet functions selected via the proposed criterion (both in numerical and experimental scenarios) can detect the position of damages in the beam structure with high accuracy.
... Thus, it will be easier to find the optimum value. Therefore, different PSO methods have come to the fore recently [25][26][27]. ...
Direct current (DC) motors have superior features such as operating at different speeds, being affordable and easily controllable. Therefore, DC motors have many uses, such as machine tools and robotic systems in many factories up to the textile industry. The PID controller is one of the most common methods used to control DC motors. PID is a feedback controller with the terms Proportional, Integral, and Derivative. The proper selection of P, I, and D parameters is critical for achieving the desired control in the PID controller. In this study, the transfer function of a DC motor is first obtained, and the speed of the DC motor is controlled by the PID controller using this transfer function. Then, Particle Swarm Optimization (PSO), an optimization method based on swarm intelligence, is used to adjust the P, I, and D parameters. By using the obtained P, I, and D coefficients, the speed of the DC motor is tried to be controlled, and the effect of the filter coefficient on the system output is examined. The performance of the proposed PSO-PID controller with successful results is given in tables and graphics. Control and optimization studies are carried out with MATLAB Simulink.
... In order to enhance convergence and precision in the first HCO, an innovative system for modifying velocity [17] is introduced to change equation (49). This paper proposes a new variant, Initial best , consisting of the first k best sperm cells (solutions) with the highest fitness values. ...
Wireless sensor network (WSN), is utilized in a wide range of real-world uses at the present time. WSN has the capability to detect objects, collect information, analyze that information, and then communicate it once again. The importance of optimization approaches is vital for the precise and trustworthy estimation of the position of sensor nodes. The standard Distance Vector Hop (DV-Hop) localization approach is not completely accurate enough for positioning. Nevertheless, it is distinguished by its straightforwardness, durability, practicality, and low hardware prerequisites. This article presents an Improved Distance Vector Hop (IDV-Hop) localization algorithm that utilizes Variable Velocity Strategy Human Conception Optimization (VVS-HCO) to improve positioning accuracy of a sensor node without incurring additional hardware costs. To improve the convergence and precision of the original HCO, the VVS-HCO introduces a new technique for updating velocity. The suggested method provides a parameter that may be adjusted to change the hop size of the anchor nodes. In addition, it analyzes using the traditional distance vector hop algorithm, the improved distance vector hop algorithm, the distance vector hop based Particle Swarm Optimization (PSO), the distance vector hop based Class Topper Optimization (CTO), the distance vector hop based Squirrel Search Algorithm (SSA), the distance vector hop based Social Learning Class Topper Optimization (SLCTO) and the distance vector hop based original Human Conception Optimization (HCO) algorithm. The simulation results confirm that the proposed algorithm outperforms the competing algorithms by reducing the localization error, localization error variance, and improving the localization accuracy with varying the number of anchor nodes, total number of nodes, and the communication range.
... Nadjafi et al. [122] evaluated the damage in a beam structure using the PSO algorithm with the curvature of modal flexibility as the objective function. Minh et al. [123] proposed a variable velocity strategy particle swarm optimization (VVS-PSO) algorithm, which further improved the computational efficiency and accuracy of the PSO method for identifying structural damage. Daei and Mirmohammadi [124] proposed a continuous ACO algorithm for identifying structural damage with modal flexibility as the objective function. ...
Material damage in structures must be detected in a timely manner to prevent engineering accidents. Damage detection based on structural flexibility has attracted widespread attention in recent years due to its simplicity and practicality. This article provides a detailed overview of damage detection methods based on structural flexibility. Depending on the calculation method and data used, flexibility-based methods can be divided into the following categories: flexibility difference, flexibility derivative index, flexibility sensitivity, flexibility decomposition, static flexibility, and combinations of flexibility with other methods. The basic principles and main calculation formulas of various flexibility methods are explained, and their advantages and disadvantages are analyzed. For the method using flexibility difference, the advantage is that the calculation is very simple and does not require the construction of a finite element model of the structure. The disadvantage is that it requires the measurement of modal data of the intact structure, and this method cannot quantitatively assess the degree of damage. For the method using the flexibility derivative index, the advantage is that it only requires the modal data of the damaged structure to locate the damage, but this method is particularly sensitive to noise in the data and is prone to misjudgment. For methods based on flexibility sensitivity and flexibility decomposition, the advantage is that they can simultaneously obtain the location and degree of damage in the structure, but the disadvantage is that they require the establishment of accurate finite element models in advance. Static flexibility methods can compensate for the shortcomings of dynamic flexibility methods, but they usually affect the normal use of the structure during static testing. Combining flexibility-based methods with advanced intelligent algorithms and other methods can further improve their accuracy and efficiency in identifying structural damage. Finally, this article discusses the challenges that have not yet been solved among damage detection methods based on structural flexibility.
... The identification of damage in a truss, frame, and plate structure was accomplished by Dinh-Cong and Nguyen-Thoi [32] through the utilization of the chaos game optimization (CGO) algorithm. Minh et al. [33] also presented a variable velocity strategy particle swarm optimization (VVS-PSO) algorithm to address the damage detection problem of building frame structures. ...
Dams are among the most important civil engineering structures, but they are also susceptible to damage that can endanger their stability and functionality. It is therefore crucial to detect any damage in dams to ensure their long-term integrity and overall safety. In light of this, the present study proposes a new two-stage method for identifying damage in concrete gravity dams. In the first stage, a modal curvature-based damage index (MCBDI) is presented to detect the location of suspected damaged zones along the height of the dam. The mode shape data collected from some measurement points on the downstream side of the dam is used to accomplish this task. In the second stage, the pathfinder algorithm (PFA) as a powerful meta-heuristic optimization technique is applied to determine the severity of potentially damaged zones by minimizing an objective function specified in terms of natural frequencies and mode shapes. The capability and effectiveness of the proposed method are evaluated by implementing two numerical simulation examples of concrete gravity dams under both noise-free and noisy conditions. The results obtained suggest that the proposed two-stage method, comprising the MCBDI indicator and the PFA algorithm, represents an accurate and efficient approach for localizing and quantifying damage in concrete gravity dams.
... To confirm that the module gain coefficients can be appointed within a considerable range of values, the upper bounds u b and lower bounds l b are selected as 1×10 6 and 1×10 3 , respectively. A GWO and PSO techniques detailed can be found in [48][49][50][51][52][53][54][55][56][57][58][59]. Furthermore, the selected parameters of both methods are recorded in Table 1 and Table 2, respectively. ...
This study aims to unbalanced power quality (PQ) conditions analysis of solar photovoltaic arrays and battery energy storage system (PV-BESS) integrated active power filter module (APFM). Here, the APFM's role is to mitigate the PQ issues that existed by the nonlinear loads. The standalone PV-APFM design is negligibly reliable approximated to a hybrid PV-BESS system because of its fluctuation and high environmental reliance. Further, here the research challenge is to optimise the APFM controller gain coefficients by the grey wolf optimization (GWO) algorithm and apply this technique to current and voltage harmonic loops (CHL-VHL) to achieve the best answer to enhance the unbalanced PQ conditions. The multi-objective functions (MOFs) to crack the problem include the total harmonic distortion (THD) of current and voltage components. The particle swarm optimization (PSO) approach will be considered a complementary method for validation and comparison with the outcomes obtained from the GWO procedure. Four case studies in imbalance conditions have been considered to confirm the proposed method in MATLAB-Simulink software.
