Continuous advancements in GNSS systems have led, apart from the broadly used GPS, to the development of other satellite systems (Galileo, BeiDou, GLONASS), which have significantly increased the number of available s...
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Continuous advancements in GNSS systems have led, apart from the broadly used GPS, to the development of other satellite systems (Galileo, BeiDou, GLONASS), which have significantly increased the number of available satellites for GNSS positioning applications. However, despite GNSS satellites' redundancy, a potential poor GNSS satellite signal (i.e., low signal-to-noise ratio) can negatively affect the GNSS's performance and positioning accuracy. On the other hand, selecting high-quality GNSS satellite signals by retaining a sufficient number of GNSS satellites can enhance the GNSS's positioning performance. Various methods, including optimization algorithms, which are also commonly adopted in artificial intelligence (AI) methods, have been applied for satellite selection. In this study, five optimization algorithms were investigated and assessed in terms of their ability to determine the optimal GNSS satellite constellation, such as Artificial Bee Colony optimization (ABC), Ant Colony optimization (ACO), Genetic algorithm (GA), Particle Swarm optimization (PSO), and Simulated Annealing (SA). The assessment of the optimization algorithms was based on two criteria, such as the robustness of the solution for the optimal satellite constellation and the time required to find the solution. The selection of the GNSS satellites was based on the weighted geometric dilution of precision (WGDOP) parameter, where the geometric dilution of precision (GDOP) is modified by applying weights based on the quality of the satellites' signal. The optimization algorithms were tested on the basis of 24 h of tracking data gathered from a permanent GNSS station, for GPS-only and multi-GNSS data (GPS, GLONASS, and Galileo). According to the comparison results, the ABC, ACO, and PSO algorithms were equivalent in terms of selection accuracy and speed. However, ABC was determined to be the most suitable algorithm due it requiring the fewest number of parameters to be set. To further investiga
Due to the huge popularity of wireless networks, future designs will not only consider the provided capacity, but also the induced exposure, the corresponding power consumption, and the economic cost. As these require...
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Due to the huge popularity of wireless networks, future designs will not only consider the provided capacity, but also the induced exposure, the corresponding power consumption, and the economic cost. As these requirements are contradictory, it is not straightforward to design optimal wireless networks. Those contradicting demands have to satisfy certain requirements in practice. In this paper, a combination of two algorithms, a genetic algorithm and a quasi-particle swarm optimization, is developed, yielding a novel hybrid algorithm that generates further optimizations of indoor wireless network planning solutions, which is named hybrid indoor genetic optimization algorithm. The algorithm is compared with a heuristic network planner and composite differential evolution algorithm for three scenarios and two different environments. Results show that our hybrid-algorithm is effective for optimization of wireless networks which satisfy four demands: maximum coverage for a user-defined capacity, minimum power consumption, minimal cost, and minimal human exposure.
Traditional laboratory tests for measuring rock uniaxial compressive strength(UCS)are tedious and *** is a pressing need for more effective methods to determine rock UCS,especially in deep mining environments under hi...
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Traditional laboratory tests for measuring rock uniaxial compressive strength(UCS)are tedious and *** is a pressing need for more effective methods to determine rock UCS,especially in deep mining environments under high in-situ ***,this study aims to develop an advanced model for predicting the UCS of rockmaterial in deepmining environments by combining three boosting-basedmachine learning methods with four optimization *** this purpose,the Lead-Zinc mine in Southwest China is considered as the case *** density,P-wave velocity,and point load strength index are used as input variables,and UCS is regarded as the ***,twelve hybrid predictive models are *** mean square error(RMSE),mean absolute error(MAE),coefficient of determination(R2),and the proportion of the mean absolute percentage error less than 20%(A-20)are selected as the evaluation *** results showed that the hybridmodel consisting of the extreme gradient boostingmethod and the artificial bee colony algorithm(XGBoost-ABC)achieved satisfactory results on the training dataset and exhibited the best generalization performance on the testing *** values of R2,A-20,RMSE,and MAE on the training dataset are 0.98,1.0,3.11 MPa,and 2.23MPa,*** highest values of R2 and A-20(0.93 and 0.96),and the smallest RMSE and MAE values of 4.78 MPa and 3.76MPa,are observed on the testing *** proposed hybrid model can be considered a reliable and effective method for predicting rock UCS in deep mines.
Study Region: The upper reaches of the Shaying River Basin (the USR Basin) in the Huai River Basin, China Study Focus: The calibration of model parameters is one of the key challenges in advancing the application of h...
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Study Region: The upper reaches of the Shaying River Basin (the USR Basin) in the Huai River Basin, China Study Focus: The calibration of model parameters is one of the key challenges in advancing the application of hydrological models. The study proposes a novel metaheuristic optimization algorithm, the chaotic particle swarm genetic algorithm (CPSGA), and compares its performance with four well-known optimization algorithms in the field of hydrological model calibration: GA, PSO, DE, and SCE-UA. The comparison focuses on effectiveness, stability, time consumption, and convergence characteristics. New Hydrological Insights for the Region: During the parameter calibration process of runoff simulation in the USR Basin, CPSGA demonstrates strong effectiveness and convergence characteristics. It enhances the GA framework by integrating initial population chaotization, perturbation evolution, and sub-adaptation strategies, which improve individual diversity and facilitate targeted evolution, thereby increasing effectiveness and convergence. However, these enhancements compromise stability and increase time consumption compared to other algorithms. While PSO shows the best convergence characteristics, it suffers from reduced swarm diversity in later iterations, leading to local optima and poor effectiveness. The complex concept and competitive complex evolution (CCE) strategy of SCE-UA make it less effective for optimization problems with a considerable number of variables, limiting its suitability for calibrating fully distributed hydrological models. These results can provide reference for parameter calibration and uncertainty analysis in distributed hydrological models.
In the realm of ocean engineering and environmental research, the accuracy of the wave spectrum is paramount. Traditional models, constrained by the number of parameters, often struggle to accurately capture the intri...
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In the realm of ocean engineering and environmental research, the accuracy of the wave spectrum is paramount. Traditional models, constrained by the number of parameters, often struggle to accurately capture the intricacies of real-world wave spectra, especially when confronting the complexities of multi-peak spectral shapes shaped by wind-sea and swell systems. This research develops a novel multi-parameter wave spectrum model that substantially amplifies the capacity to describe the diversity of wave spectra by introducing more shape parameters. The proposed model includes more shape parameters than its predecessors. These parameters can be directly optimized and obtained through the Heterogeneous Comprehensive Learning Particle Swarm Optimizer (HCLPSO). This sophisticated algorithm leverages its robust global search capabilities and swift convergence to fine-tune the model's parameters, ensuring a high degree of precision in fitting the measured wave spectra. Furthermore, the proposed model also shows great potential in wave spectrum prediction, even when there are fewer training samples, it can still yield relatively accurate predictive results. This research not only improves the applicability and accuracy of the wave spectrum model but also offers a new tool for ocean wave research and real-world applications, thereby contributing important research and practical value to the field.
To address the challenge of the "curse of dimensionality" in aerodynamic design optimization of compressors, this study introduces an innovative optimization technique suitable for compressor airfoil design....
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To address the challenge of the "curse of dimensionality" in aerodynamic design optimization of compressors, this study introduces an innovative optimization technique suitable for compressor airfoil design. This technique, rooted in a hybrid mechanism-data-driven approach, seamlessly integrates a hierarchical parameterization method, based on elliptic topological deformation, into a multitasking evolutionary algorithm framework. This integration deviates from the conventional approach of treating parameterization methods and optimization algorithms as distinct elements. The proposed method positions airfoil parameterization as its core, constructing two tasks within the optimization algorithm. It leverages the critical influence of the parameterization method on the aerodynamic performance landscape of the airfoils and the intrinsic qualities of the hierarchical parameterization method in the design space. The multitasking evolutionary optimization framework facilitates effective information exchange between tasks, significantly boosting optimization efficiency. In comparison to standard data-driven multitasking evolutionary algorithms, the proposed method achieves superior optimized solutions with merely 11 x D aerodynamic performance evaluations, where D denotes the number of design variables.
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.
The orthogonality of transmitted waveforms is an important factor affecting the performance of MIMO radar systems. The orthogonal coded signal is a commonly adopted waveform in MIMO radar, and its orthogonality depend...
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The orthogonality of transmitted waveforms is an important factor affecting the performance of MIMO radar systems. The orthogonal coded signal is a commonly adopted waveform in MIMO radar, and its orthogonality depends on the used orthogonal discrete code sequence set (ODCSs). Among existing optimization algorithms for ODCSs, the results designed by the greedy code search-based memetic algorithm (MA-GCS) have exhibited the best autocorrelation and cross-correlation properties observed so far. Based on MA-GCS, we propose a novel hybrid algorithm called the memetic algorithm with iterative greedy code search (MA-IGCS). Extensions involve replacing the greedy code search used in MA-GCS with a more efficient approach, iterative greedy code search. Furthermore, we propose an "individual uniqueness strategy" and incorporate it into our algorithm to preserve population diversity throughout iteration, thereby preventing premature stagnation and ensuring the continued pursuit of feasible solutions. Finally, the design results of our algorithm are compared with the MA-GCS. Experimental results demonstrate that the MA-IGCS exhibits superior search capability and generates more favorable design results than the MA-GCS.
Interdisciplinary integration is a superior method to improve the optimization algorithm. In this paper, control theory and optimization are combined, and the optimization algorithm is regarded as a control process. B...
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Interdisciplinary integration is a superior method to improve the optimization algorithm. In this paper, control theory and optimization are combined, and the optimization algorithm is regarded as a control process. Based on the premise of optimal control, the state equation corresponding to Lagrange algorithm is established with the Karush-Kuhn-Tucker (KKT) conditions as the objective. As an optimal control method, linear quadratic regulator (LQR) is utilized to control the calculation process, and an innovative LQR-Lagrange algorithm is proposed. The Lyapunov stability criterion is applied to analyze the convergence, and it is proved that the proposed LQR-Lagrange algorithm is bound to converge as long as the parameter matrices Q and R are positive definite. The analysis indicates that the influence of parameters in LQR-Lagrange algorithm on the calculation speed is monotonic, and the elements in Q and R has no effect on the convergence. Therefore, the proposed algorithm has a monotonic and user-friendly parameter tuning strategy. It perfectly tackles the game between parameter tuning strategy and calculation speed, and cracks the difficulties and dilemmas of conventional algorithms in this issue, realizing a win-win situation.
Reservoir history matching represents a crucial stage in the reservoir development process and purposes to match model predictions with various observed field data, including production, seismic, and electromagnetic d...
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Reservoir history matching represents a crucial stage in the reservoir development process and purposes to match model predictions with various observed field data, including production, seismic, and electromagnetic data. In contrast to the traditional manual approach, automatic history matching (AHM) significantly reduces the workload of reservoir engineers by automatically tuning the reservoir model parameters. AHM can be viewed as an automated solution to an inverse problem, and the selection of optimization algorithms is crucial for achieving effective model matching. However, the optimization process requires running numerous simulations. Surrogate models, achieved through simplification or approximation of the realistic model, offer a significant reduction in computational costs during the simulation process. In this paper, we provide an overview of commonly prevalent optimization algorithms and surrogate models in the AHM process, presenting the latest advancements in these methods. We analyze the strengths and limitations of these approaches and discuss the future challenges and directions of AHM, aiming to provide valuable references for further research and applications in this field.
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