Quantum computing has become a pivotal innovation in computational science, offering novel avenues for tackling the increasingly complex and high-dimensional optimization challenges inherent in engineering design. Thi...
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Quantum computing has become a pivotal innovation in computational science, offering novel avenues for tackling the increasingly complex and high-dimensional optimization challenges inherent in engineering design. This paradigm shift is particularly pertinent in the domain of structural optimization, where the intricate interplay of design variables and constraints necessitates advanced computational strategies. In this vein, the gate-based variational quantum algorithm utilizes quantum superposition and entanglement to improve search efficiency in large solution spaces. This paper delves into the gate-based variational quantum algorithm for the discrete variable truss structure size optimization problem. By reformulating this optimization challenge into a quadratic, unconstrained binary optimization framework, we bridge the gap between the discrete nature of engineering optimization tasks and the quantum computational paradigm. A detailed algorithm is outlined, encompassing the translation of the truss optimization problem into the quantum problem, the initialization and iterative evolution of a quantum circuit tailored to this problem, and the integration of classical optimization techniques for parameter tuning. The proposed approach demonstrates the feasibility and potential of quantum computing to transform engineering design and optimization, with numerical experiments validating the effectiveness of the method and paving the way for future explorations in quantum-assisted engineering optimizations.
In this study, a novel artificial meerkat optimization algorithm (AMA) is proposed to simulate the cooperative behaviors of meerkat populations. The AMA algorithm is designed with two sub-populations, multiple search ...
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In this study, a novel artificial meerkat optimization algorithm (AMA) is proposed to simulate the cooperative behaviors of meerkat populations. The AMA algorithm is designed with two sub-populations, multiple search strategies, a multi-stage elimination mechanism, and a combination of information sharing and greedy selection strategies. Drawing inspiration from the intra-population learning behavior, the algorithm introduces two search mechanisms: single-source learning and multi-source learning. Additionally, inspired by the sentinel behavior of meerkat populations, a search strategy is proposed that combines Gaussian and L & eacute;vy variations. Furthermore, inspired by the inter-population aggression behavior of meerkat populations, the AMA algorithm iteratively applies these four search strategies, retaining the most suitable strategy while eliminating others to enhance its applicability across complex optimization problems. Experimental results comparing the AMA algorithm with seven state-of-the-art algorithms on 53 test functions demonstrate that the AMA algorithm outperforms others on 71.7% of the test functions. Moreover, experiments on challenging engineering optimization problems confirm the superior performance of the AMA algorithm over alternative algorithms.
This paper presents an extensive study on the electrochemical, shunt currents, and hydraulic modeling of a vanadium redox flow battery of m stacks and n cells per stack. The shunt currents model of the battery has bee...
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This paper presents an extensive study on the electrochemical, shunt currents, and hydraulic modeling of a vanadium redox flow battery of m stacks and n cells per stack. The shunt currents model of the battery has been developed through the use of Kirchoff's laws, taking into account the different design cases that can occur and enumerating the equations of nodes and meshes specifying them so that the software implementation can be performed in a direct way. The hydraulic model has been developed by numerical methods. These models are put to work simultaneously in order to simulate the behavior of a VRFB battery during charging and discharging, obtaining the pressure losses and shunt currents that occur in the battery. Using these models, and by using a PSO-type optimization algorithm, specifically designed for discrete variables, the battery design is optimized in order to minimize the round-trip efficiency losses due to pressure losses and shunt currents. In the optimization of the battery design, value is given to the number of stacks in which the total number of cells in the battery is distributed and the dimensions of the piping relative to both the stacks and the cells.
Artificial Ground Freezing (AGF) is a promising method for controlling seepage in permeable strata. However, AGF faces challenges, including difficulties in achieving a frozen barrier in high-flow conditions and conce...
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Artificial Ground Freezing (AGF) is a promising method for controlling seepage in permeable strata. However, AGF faces challenges, including difficulties in achieving a frozen barrier in high-flow conditions and concerns about cost-effectiveness. This study optimizes freezing pipe placement in AGF using a simulated annealing algorithm and a coupled hydrothermal finite element model, focusing on AGF system responses under varying seepage velocities. The optimized layout significantly reduces freeze-ring formation time (by 2.5 days) and the overall freezing duration (by 12.5 days). Moreover, it substantially decreases the required frozen soil volume, facilitating drilling and excavation. Across different seepage velocities, the difference in freeze-ring formation time between the optimized and uniform layouts gradually increases with higher seepage velocity, reaching a maximum difference of 5.9 days. Finally, the relationship between freezing time and seepage velocity was quantitatively described using exponential functions. This study underscores the critical role of optimizing freezing pipe placement in AGF, providing a foundation for efficient and cost-effective geotechnical engineering practices.
The development of vibration suppression systems with desired efficiency and low cost is one of the significant challenges in engineering. In this study, a parametric lattice model is considered to analyze the wave mi...
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The development of vibration suppression systems with desired efficiency and low cost is one of the significant challenges in engineering. In this study, a parametric lattice model is considered to analyze the wave mitigation features in the metamaterial based on a tetra-chiral topology of the periodic cell equipped with internal resonators. Bloch wave theorem and finite element method are employed to explore the bandgap of the structure and its wave mitigation features. Since the unit cell geometry can be designed to open and shift bandgaps, particle swarm optimization algorithm is used to find the largest possible gap in the desired frequency range. The optimization method is programmed using MATLAB combined with an in-house finite element solver, considering the parameters' ranges to ensure geometric compatibility. In all studied cases, the optimized geometry leads to superior vibration suppression and larger complete bandgaps.
Wind farm layout optimization is essential for improving power generation efficiency and reducing operational costs in wind farms. This study develops a multi-turbine wake superposition model incorporating turbine yaw...
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Wind farm layout optimization is essential for improving power generation efficiency and reducing operational costs in wind farms. This study develops a multi-turbine wake superposition model incorporating turbine yaw effects, based on a three-dimensional polynomial wake model. Dynamic weight and Levy flight strategies are employed to enhance the sparrow search algorithm (SSA) for layout optimization. The proposed wake model is validated with experimental data, and the superiority of DLSSA is confirmed through comparisons with traditional algorithms. Parameter analysis of active yaw strategy is conducted using two tandem wind turbines. Integrating DLSSA with the wake model, layout optimization considering height variation and active yawing strategies is investigated using dimensionless annual energy production (DAEP) as the objective function. Simulation data suggests that optimal total power output is attained when two wind turbines are positioned in a tandem configuration, with the upstream turbine set at a yaw angle of 15 degrees. Incorporating height variation and active yaw control significantly enhances the total power output of wind farms. Implementing these strategies in layout optimization can increase total power output by 1.32 %-10.86 % compared to alternative layouts. Notably, joint optimization surpasses sequential optimization, resulting in a 1.70 % higher total power output.
In this study, we investigate the performance of different optimization algorithms in estimating the Markov switching (MS) deterministic components of the traditional ADF test. For this purpose, we consider Broyden, F...
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In this study, we investigate the performance of different optimization algorithms in estimating the Markov switching (MS) deterministic components of the traditional ADF test. For this purpose, we consider Broyden, Fletcher, Goldfarb, and Shanno (BFGS), Berndt, Hall, Hall, Hausman (BHHH), Simplex, Genetic, and Expectation-Maximization (EM) algorithms. The simulation studies show that the Simplex method has significant advantages over the other commonly used hill-climbing methods and EM. It gives unbiased estimates of the MS deterministic components of the ADF unit root test and delivers good size and power properties. When Hamilton's (Econometrica 57:357-384, 1989) MS model is re-evaluated in conjunction with the alternative algorithms, we furthermore show that Simplex converges to the global optima in stationary MS models with remarkably high precision and even when convergence criterion is raised, or initial values are altered. These advantages of the Simplex routine in MS models allow us to contribute to the current literature. First, we produce the exact critical values of the generalized ADF unit root test with MS breaks in trends. Second, we derive the asymptotic distribution of this test and provide its invariance feature.
Small parasitic Hemipteran insects known as bedbugs(Cimicidae)feed on warm-blooded mammal’s *** most famous member of this family is the Cimex lectularius or common *** current paper proposes a novel swarm intelligen...
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Small parasitic Hemipteran insects known as bedbugs(Cimicidae)feed on warm-blooded mammal’s *** most famous member of this family is the Cimex lectularius or common *** current paper proposes a novel swarm intelligence optimization algorithm called the Bedbug Meta-Heuristic algorithm(BMHA).The primary inspiration for the bedbug algorithm comes from the static and dynamic swarming behaviors of bedbugs in *** two main stages of optimization algorithms,exploration,and exploitation,are designed by modeling bedbug social interaction to search for *** proposed algorithm is benchmarked qualitatively and quantitatively using many test functions including *** results of evaluating BMHA prove that this algorithm can improve the initial random population for a given optimization problem to converge towards global optimization and provide highly competitive results compared to other well-known optimization *** results also prove the new algorithm's performance in solving real optimization problems in unknown search *** achieve this,the proposed algorithm has been used to select the features of fake news in a semi-supervised manner,the results of which show the good performance of the proposed algorithm in solving problems.
Wireless sensor networks are becoming increasingly popular across a range of applications. One notable use is in seismic exploration and monitoring for oil and gas reservoirs. This application involves deploying numer...
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Wireless sensor networks are becoming increasingly popular across a range of applications. One notable use is in seismic exploration and monitoring for oil and gas reservoirs. This application involves deploying numerous sensor nodes across outdoor fields to measure backscattered waves, which are then used to create an image of the subsurface. These sensor nodes remain active in the field for several days and must be accurately localized to ensure efficient reservoir detection. However, the Distance Vector-Hop (DVHop) algorithm, despite its simplicity, is not suitable for accurate node localization in exploration fields due to obstructions. In this paper, we propose a modified DVHop algorithm specifically designed for precise localization in such environments. Proposed algorithm uses angles between intermediate nodes to identify and circumvent nodes affected by obstructions. Distance estimation is performed using this reduced set of nodes. The estimated distances between these nodes are subsequently solved using Velocity Pausing Particle Swarm optimization to determine the nodes' locations. When evaluated in environments resembling exploration fields, our algorithm demonstrated an improvement of 25% to 63% in Average Localization Accuracy compared to other hop-based localization algorithms under similar conditions. A unique approach to minimize the impact of obstructions in estimating the locations of a randomly formed WSN. A novel method for node localization using a newly developed optimization algorithm called VPPSO. The applicability of the algorithm for detecting oil and gas reservoirs has been tested.
Due to the increasing threat of malware to computer systems and networks, traditional malware detection and recognition technologies face difficulties and limitations. Therefore, exploring new methods to improve the a...
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Due to the increasing threat of malware to computer systems and networks, traditional malware detection and recognition technologies face difficulties and limitations. Therefore, exploring new methods to improve the accuracy and efficiency of malware identification has become an urgent need. This study introduces ant colony algorithm to optimize traditional clustering algorithms and algorithm parameters. The experimental results showed that the improvement rates of the improved algorithm in accuracy, echo value, and false alarm rate were 0.253, 0.115, and 0.056, respectively. The accuracy on the training and validation sets continued to increase and the loss curve continued to decrease. In addition, the improved algorithm had stronger modeling ability for data feature relationships and temporal information. This is of great help in improving the recognition ability of virus and worm software. The improved algorithm had a lower occupancy rate of computing resources compared to other algorithms, but it could also effectively monitor device operation. Compared with traditional methods, this method can more accurately identify malicious software and effectively identify malicious software samples from large-scale datasets. This is of great significance for protecting computer systems and network security.
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