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Illustration of updating the position of an ω wolf, based on the positions of the three best agents (α, β, δ wolves) with respect to the estimated position of prey in a two-dimensional space.
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![FIGURE. 5. Pseudo code of the GWO [33].](profile/Agathoklis-Giaralis/publication/356887914/figure/fig3/AS:1098915445243907@1639013267627/Pseudo-code-of-the-GWO-33_Q320.jpg)
In recent decades, active vibration control of buildings for earthquake-induced damage mitigation has been widely considered in the scientific literature. Fuzzy logic control (FLC) has been shown to be an effective approach to regulate control forces exerted by actuators to building structures to reduce earthquake-borne oscillations. In many cases,...
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... graphically illustrated in Fig.4. The pseudo code of the GWO is provided in Fig. 5, [33]. ...
Context 2
... at the start of the kth iteration the three best solutions obtained thus far, í µí± í µí± ⃗⃗⃗⃗⃗ (í µí±) , í µí± í µí»½ ⃗⃗⃗⃗⃗ (í µí±) and í µí± í µí»¿ ⃗⃗⃗⃗⃗ (í µí±) are selected based on the values of the objective function (fitness of solution) at the current location of all the agents and the position of all the agents are updated according to the location of the three best search agents, equally weighted. This is mathematically expressed, for the case of an arbitrary agent with position í µí± ⃗ (í µí±) , by the set of equations [ and graphically illustrated in Fig.4. The pseudo code of the GWO is provided in Fig. 5 [33]. ...
Citations
... The active protective system includes the devices such as controllers, actuators and sensors [15]. In this system, an external power supply is needed for the operational purpose and needs to be active during the dynamic wind and earthquake load [16], 17. A small amount of external energy or power source is used by the semi-active control system for its operation and consumes a moment of the structure to produce the control force, where the control force can be manipulated by an external power source. ...
Robustness is defined as the insensitivity of the structure to uncertainties like earthquakes, fire, cyclones, explosions, tsunamis, etc. Robustness analysis of Kalman observer with robust controller for damped outrigger structure is studied to analyze its design performance in the presence of various effects like an earthquake. This is a novel innovative study undertaken in evaluating the robustness of the proposed controller to signify its performance in the field of structural control. The damped outrigger structure is modeled using the finite element approach by finding mode shapes, fundamental natural frequency, and period. The Kalman observer is modeled according to the requirement of the structure with the Riccati equation. The robust proportional–integral–derivative controller is designed according to the input disturbance with Ziegler–Nichols ultimate gain approach. The issue of deterioration in the system performance due to saturation was observed during the analytical investigation of the robust proportional–integral–derivative controller with Kalman observer, which has been addressed by the anti-windup approach. The robustness index of the structure is calculated using sensitivity and complementary sensitivity. The maximum amplitude ratio of the sensitivity for viscous damper-controlled structure is 1.4723 and the value decreases for the other controllers, with a minimum value of 1.0 for the proposed anti-windup robust proportional–integral–derivative controller with Kalman observer. Respectively, the percentage overshoot for the uncontrolled case is 23.4% that values decreasing for other controlled cases, with a minimum of 7.8% for proposed anti-windup robust proportional–integral–derivative controller with Kalman observer. This robustness index and performance indices discriminate the significant robust performance of the proposed robust proportional–integral–derivative controller with Kalman observer-based damped outrigger structure in comparison with other controlled and uncontrolled cases.
... Using optimization algorithms, researchers have studied the effects of a wide range of variables on seismic behavior models and optimizations, including fundamental period, target ductility demand, number of stories, damping ratio, post-yield behavior, and seismic excitations (Hajirasouliha & Moghaddam, 2009). In addition, a better metaheuristic optimization technique has been created to construct a fuzzy logic controller to effectively safeguard tall structures from earthquakes (Azizi et al., 2021). ...
... In this study, Azizi et al. (2021) provide an enhanced version of the Grey Wolf Optimizer (GWO) algorithm, specifically designed to mitigate vibrations in building structures subjected to seismic excitations actively. The Grey Wolf Optimization (GWO) algorithm is a metaheuristic approach that draws inspiration from the hunting behavior of grey wolves. ...
In seismic structural engineering, there is a significant issue in comprehending the behavior of thin-walled rectangular hollow bridge piers within the context of dynamic phenomena. This research aimed to investigate a complex behavior using recurrent neural networks (RNNs) in conjunction with metaheuristic algorithms, namely the charged system search (CSS) and the black hole algorithm (BHA), to optimize the analysis. The approach used in this study included a rigorous process of data preprocessing to enhance the quality of seismic datasets and the development of an RNN model optimized utilizing the metaheuristics above. The results of the study were significant. The combined use of the RNN-CSS and RNN-BHA models showed enhanced prediction capacities compared to solo RNNs, thereby emphasizing the effectiveness of integrating neural networks with global optimization approaches. In addition, the convergence, diversity, search space, and sensitivity studies provided further insights into the modeling technique’s stability, comprehensiveness, and dependability. In summary, our study signifies a novel shift in seismic structural modeling, emphasizing the prospects of using multidisciplinary approaches to forecast and comprehend the hysteresis characteristics of bridge piers subjected to seismic stresses.
... In pursuit, Azizi et al. [22] proposed an upgraded gray wolf optimizer (UGWO) that improved the performance of the standard gray wolf optimizer with respect to the seismic control of vibration in nonlinear structures. Their comparisons revealed that the UGWO outperformed other optimizers and resulted in less damage to the benchmark structures. ...
In the semiconductor industry, the vulnerability of high-tech facilities installed on platforms to ground excitations induced by nearby traffic is significantly pronounced, primarily due to their small-scale dimensions. Consequently, it is imperative to design a smart control technique by effectively utilizing model-free controllers. Recently, adaptive intelligent control algorithms have emerged as a viable alternative to conventional model-based control algorithms. To address this issue, this study meticulously designed a hybrid platform using the adaptive intelligent controller known as the brain emotional learning-based intelligent controller, along with a Sugano fuzzy inference system to effectively optimize the controller’s learning parameters. These learning and intelligent-based algorithms offer notable advantages, including the ability to handle nonlinearity, uncertainty, and training capabilities within the control systems. To assess the effectiveness of the proposed controller in mitigating vibrations induced by traffic on high-tech facilities, a three degree-of-freedom structure is employed along with the hybrid platform. Finally, the performance of the hybrid platform in terms of microvibration control levels is meticulously validated using the Bolt Beranek Newman vibration criteria. Simulation results unequivocally demonstrate that the proposed controller outperforms both an uncontrolled system and a traditional linear quadratic regulator controller in terms of reducing the traffic-induced response of the hybrid platform and second floor, respectively. Through the integration of learning and intelligent-based controllers, the velocity levels of both the hybrid platform and the second floor are reduced to approximately 49.02% and remain well within the acceptable standard criteria curves.
... To improve its searching efficiency and capacities in dealing with the design membership problem and optimizing seismic structure damage, Azizi et al. [145] suggested an improved GWO. Twenty benchmark datasets with nonlinear behavior involving up to 400 design variables were used to evaluate the suggested methodology. ...
The Grey Wolf Optimizer (GWO) has emerged as one of the most captivating swarm intelligence methods, drawing inspiration from the hunting behavior of wolf packs. GWO’s appeal lies in its remarkable characteristics: it is parameter-free, derivative-free, conceptually simple, user-friendly, adaptable, flexible, and robust. Its efficacy has been demonstrated across a wide range of optimization problems in diverse domains, including engineering, bioinformatics, biomedical, scheduling and planning, and business. Given the substantial growth and effectiveness of GWO, it is essential to conduct a recent review to provide updated insights. This review delves into the GWO-related research conducted between 2019 and 2022, encompassing over 200 research articles. It explores the growth of GWO in terms of publications, citations, and the domains that leverage its potential. The review thoroughly examines the latest versions of GWO, categorizing them based on their contributions. Additionally, it highlights the primary applications of GWO, with computer science and engineering emerging as the dominant research domains. A critical analysis of the accomplishments and limitations of GWO is presented, offering valuable insights. Finally, the review concludes with a brief summary and outlines potential future developments in GWO theory and applications. Researchers seeking to employ GWO as a problem-solving tool will find this comprehensive review immensely beneficial in advancing their research endeavors.
To improve the performance and durability of vehicle components, efforts have been made to reduce driveline oscillations using advanced active control algorithms. However, existing methods often rely on subjective parameter adjustments, which can be burdensome for designers. This study introduces an effective tuning algorithm for a driveline vibration controller that accounts for nonlinear backlash effects. Initially, a driveline dynamics model is developed to focus on transient oscillations resulting from changes in driving force and the presence of nonlinear backlash. The backlash impact is incorporated into the model through a discontinuous dead-zone region. Two operational dynamics, which are the contact mode and the backlash mode, are considered. A dynamic output feedback H2 controller is designed as a baseline controller to mitigate low-frequency resonance in the driveline. A solution for managing the nonlinear backlash challenges is introduced, involving the use of a simple control mode switching algorithm in conjunction with the controller. This algorithm relies on a time-dependent-switched Kalman filter. Additionally, the optimal settings for the parameters needed by the mode-switching algorithm are autonomously determined using the grey wolf optimizer (GWO). The proposed active controller can be implemented in real vehicles by using an on-vehicle acceleration sensor and electronic control unit (ECU). In a simulation environment, the vehicle body vibration is online fed back to the resultant controller, and an actuator is supposed to apply control commands to the driveline. The effectiveness of this newly proposed active controller is confirmed through comparative tests, revealing the superior vibration control.
The current study uses a recently-developed metaheuristic method called Crystal Structure Algorithm (CryStAl) to achieve optimized vibration control in structural engineering. More specifically, this algorithm, which is inspired by the well-established crystallographic principles underlying the formation of crystalline solids in nature, is applied to the optimization of fuzzy logic controllers in building structures. To demonstrate the capability of this method in solving real engineering problems, two real-size building structures, one with three and the other with twenty stories, are considered. The fuzzy controllers are implemented through an active control system to control the seismically-induced vibrations of the structures intelligently. The evaluation criteria utilized to assess the overall performance of the optimization method applied to the fuzzy control system are presented and discussed. Through nonlinear structural analyses, the ductility, energy dissipation, and other nonlinear characteristics of the structures are also considered as the structural responses to be controlled. The computational results obtained from this novel metaheuristic algorithm are compared with those of the other expert systems from the optimization literature. The findings of this paper demonstrate that the Crystal Structure Algorithm is capable of outranking the other methods in the majority of considered cases.
