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.
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.
As the thermal analysis model of satellites is used as an important indicator for thermal design, it must accurately simulate the thermal behaviour of actual satellites for precise thermal design. To increase the accu...
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As the thermal analysis model of satellites is used as an important indicator for thermal design, it must accurately simulate the thermal behaviour of actual satellites for precise thermal design. To increase the accuracy of the thermal analysis model, it must be correlated using the thermal balance test data for actual satellite models. To achieve this, we herein propose an efficient correlation method for satellite thermal analysis models using multiple linear regression techniques with quadratic terms and optimization algorithms. The proposed method reduces the amount of computation by choosing dominant parameters through sensitivity analysis and creating a multiple linear regression model that can replace the thermal analysis model in the subsequent optimization process. Subsequently, optimization algorithms are applied to the multiple linear regression model to perform the correlation of the thermal analysis model. In this study, the numerical validation of the proposed method was performed using numerical data from a reference thermal analysis model to verify the reliability and accuracy of the proposed method before it was applied to the correlation of the thermal analysis model using experimental data. The thermal analysis result of the reference thermal analysis model was set as the target value to correlate, and quantitative performance evaluation was performed for various combinations of optimization algorithms and design of experiments methods by comparing the estimated analysis parameters. The results of this study demonstrate that the proposed method can efficiently produce an accurate correlation model for thermal analysis.
Building energy optimization (BEO) is a promising technique to achieve energy efficient designs. The efficacy of optimization algorithms is imperative for the BEO technique and is significantly dependent on the algori...
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Building energy optimization (BEO) is a promising technique to achieve energy efficient designs. The efficacy of optimization algorithms is imperative for the BEO technique and is significantly dependent on the algorithm hyperparameters. Currently, studies focusing on algorithm hyperparameters are scarce, and common agreement on how to set their values, especially for BEO problems, is still lacking. This study proposes a metamodel-based methodology for hyperparameter optimization of optimization algorithms applied in BEO. The aim is to maximize the algorithmic efficacy and avoid the failure of the BEO technique because of improper algorithm hyperparameter settings. The method consists of three consecutive steps: constructing the specific BEO problem, developing an ANN-trained metamodel of the problem, and optimizing algorithm hyperparameters with nondominated sorting genetic algorithm II (NSGA-II). To verify the validity, 15 benchmark BEO problems with different properties, i.e., five building models and three design variable categories, were constructed for numerical experiments. For each problem, the hyperparameters of four commonly used algorithms, i.e., the genetic algorithm (GA), the particle swarm optimization (PSO) algorithm, simulated annealing (SA), and the multi-objective genetic algorithm (MOGA), were optimized. Results demonstrated that the MOGA benefited the most from hyperparameter optimization in terms of the quality of the obtained optimum, while PSO benefited the most in terms of the computing time.
In general, Metasurface Antennas (MSA) are designed to diminish the antenna shape by enhancing the operating band and directivity. As the efficiency decreases, the design complexity of MSA increases. In order to enhan...
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In general, Metasurface Antennas (MSA) are designed to diminish the antenna shape by enhancing the operating band and directivity. As the efficiency decreases, the design complexity of MSA increases. In order to enhance the antenna design, a high-gain MSA is designed using the hybrid African Vulture's optimization algorithm (AVOA), and the Capuchin Search algorithm (CapSA) is used for Radio Frequency (RF) energy harvesting. The dimensions of the designed antenna are \(1.66\lambda_optimization algorithm \times 1.25\lambda_optimization algorithm \times 0.02\lambda_optimization algorithm\) with a resonating frequency of 5 GHz. To design the high gain MSA, the proposed Hybrid African Vulture’s optimization and Capuchin Search algorithm (Hyb-AVOA-CapSA) is used to enhance the antenna parameters such as radiation efficiency, Bandwidth, gain, and return loss. Therefore, the proposed MSA design has achieved high efficiency and profit. Finally, the simulation has done on HFSS19 and ADS2020 version software; and evaluated using MATLAB. The proposed antenna gives a better efficiency of 70.12%, and resonate at 1.5 GHz of the axial ratio bandwidth at 5 GHz resonant frequency. The gain of the proposed antenna has increased from 6.86 to 7.6 dBi. While examining the comparative outcomes, the proposed approach has attained 22.4%, 23.7% high gain, and 18.85%, 12.6% lower return loss than the compared methods. Thus, the designed MSA is applied in RF energy harvesting applications because of its compact, low-profile, and simple structure. The Rectenna design uses a voltage doubler circuit at the receiver end and produces 5.55 V.
Lightweight research based on battery pack structural strength can improve the endurance and safety of electric vehicles. Based on the adaptive response surface and multi-objective particle swarm optimization algorith...
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Lightweight research based on battery pack structural strength can improve the endurance and safety of electric vehicles. Based on the adaptive response surface and multi-objective particle swarm optimization algorithm, this paper proposes an optimization design method for lightweight of battery pack shell. The thickness of the battery pack shell is the optimization parameter. The stress, strain, and frequency of the battery pack shell under typical working conditions are used as boundary conditions. The response surface model is established according to the criterion of cross terms in adaptive response surface method, and the multi-objective particle swarm optimization algorithm is used for iterative solution. The optimization results show that the maximum stress of the battery pack is reduced to the appropriate range, the first-order frequency is increased by 41% to reduce resonance, the maximum deformation is reduced from 2.7 to 1.12 mm, and the total mass is reduced by 26.8%. The battery pack optimization design method proposed in this paper can achieve lightweighting while meeting safety performance.
With the massive demand for spectrum resources due to the massive increase of wireless devices, it was necessary to manage the scarcity of radio spectrum resources. Cognitive Radio is a technology for efficiently usin...
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ISBN:
(纸本)9781665426329
With the massive demand for spectrum resources due to the massive increase of wireless devices, it was necessary to manage the scarcity of radio spectrum resources. Cognitive Radio is a technology for efficiently using the available spectrum resources in a wireless communication system. However, with the help of using various optimization algorithms, Cognitive Radio can manage and utilize the spectrum of resources more efficiently. This paper gives an overview of the state-of-art research that utilizes many optimization algorithms for different purposes such as sensing, allocating, sharing, and mobilizing the spectrum for better utilization and improving the throughput, convergence speed, delay, and minimization the interference. The main algorithms enclosed in this paper are Genetic algorithm, Particle Swarm optimization, Ant Colony optimization, and Artificial Bee Colony optimization algorithm.
In the application of moving horizon estimation (MHE) algorithm, the window length will affect the estimation accuracy and the computing efficiency. For this kind of problem, a method of parameter optimization is prop...
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In the application of moving horizon estimation (MHE) algorithm, the window length will affect the estimation accuracy and the computing efficiency. For this kind of problem, a method of parameter optimization is proposed to obtain suitable window length. Firstly, in order to facilitate online solution, the optimization problem involved in the algorithm is transformed into a quadratic programming (QP) problem in matrix form. Secondly, for the time index and the estimated residual index that measure different properties, the normalization idea is adopted to incorporate them into the same dimension to design the fitness function, and a genetic optimization algorithm based on simulated annealing mechanism is given to search for the optimal window length. Finally, the proposed parameter optimization method is verified by two cases. The results show that the parameter optimization method has the advantages of excellent local search ability and sufficient convergence, and the window length obtained by this method can better take into account the two performance indexes of the MHE algorithm and improve the estimation performance.
Since the behavior of photovoltaic (PV) modules under different operational conditions is highly nonlinear, predicting the performance of PV systems in industrial applications is becoming a major challenge issue. More...
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Since the behavior of photovoltaic (PV) modules under different operational conditions is highly nonlinear, predicting the performance of PV systems in industrial applications is becoming a major challenge issue. Moreover, the most important information required to configure an optimal PV system is unavailable in all manufacturer's datasheets. In this context, a novel method is recommended to optimize PV cells/module parameters with the ability to correctly characterize the I-V and P-V curves of different PV models. In the present article, a chaotic map is incorporated in the so-called quasi-oppositional Rao-1 algorithm to improve its efficiency, and the resulting algorithm is named quasi-oppositional logistic chaotic Rao-1 (QOLCR) algorithm. Numerical results indicate that the QOLCR algorithm has presented very good performance in terms of accuracy and robustness. The idea is to minimize the root mean square error (RMSE) between the estimated and the actual data. Simulation results in the single diode model give an RMSE of value 7.73006208 x 10(-4), and in the double diode model, an RMSE of value 7.445111655 x 10(-4) has been reached as the minimum value among the other compared optimization methods. Hence, the QOLCR approach also converges faster than the basic Rao-1 algorithm and its other variants. Moreover, the modified QO Rao-1 algorithm shows its perfectness and could be involved as tools for optimal designing of PV systems.
During fast-paced rolling of the same type strip of hot tandem rolling, the thermal expansion of the work rolls has a significant influence on the shape of the on-load roll gap, which needs to be considered in the fre...
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During fast-paced rolling of the same type strip of hot tandem rolling, the thermal expansion of the work rolls has a significant influence on the shape of the on-load roll gap, which needs to be considered in the free shifting of the work rolls. In this paper, the thermal crown evaluation index and the thermal expansion simulation model of the work roll are established, and the influence of different roll shifting parameters on the thermal crown of the roll in the service cycle of the work roll is analyzed. A special roll shifting strategy of the downstream stand is designed, and intelligent optimization is carried out with the goal of thermal roll shape and uniform wear of work rolls. The optimized roll shifting strategy can significantly improve the strip shape quality.
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