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, includinga* optimization algorithms, which are also commonly adopted in artificial intelligence (AI) methods, have been applied for satellite selection. In this study, fivea* 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 thea* 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. Thea* 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
The application ofa* optimization algorithms to adaptive motion control is proposed in this paper. In order to provide optimal system response,a* optimization algorithm is used as adjustment mechanism of controller coeffi...
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The application ofa* optimization algorithms to adaptive motion control is proposed in this paper. In order to provide optimal system response,a* optimization algorithm is used as adjustment mechanism of controller coefficients. Most ofa* optimization algorithms are not able to work in continuous optimization mode and with non-constant search space (i.e. dataset). For this reason, the introduction of a novel approach, Adaptive Procedure fora* optimization algorithms (APOA), that allows to apply most ofa* optimization algorithms to adaptation process seems to be necessary. Such an algorithm is innovative, due to its universality in terms of applieda* optimization algorithm. Its application allows to obtain optimal motion control in modern electric drives. The proposed APOA is presented together with the novel desired-response adaptive system (DRAS) approach for repetitive processes. This solution provides unchanged system response regardless of plant parameters variation or external disturbances. The developed adaptive approach based ona* optimization algorithm is implemented in permanent magnet synchronous motor (PMSM) drive to adapt state feedback speed controller during moment of inertia variations. Since the proposed DRAS is model-free approach, the adaptation procedure is immune to issues related to plant parameters mismatch. The obtained results prove that proposed adaptive speed controller for PMSM assures desired system response regardless of the moment of inertia variation. (C) 2021 ISA. Published by Elsevier Ltd. All rights reserved.
Building energy optimization (BEO) is a promising technique to achieve energy efficient designs. The efficacy ofa* 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 ofa* 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 ofa* 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.
An eigenspace optimization approach is proposed and demonstrated for the design of feedback controllers for the maneuvers/vibration arrests of flexible structures. The algorithm developed is shown to be equally useful...
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An eigenspace optimization approach is proposed and demonstrated for the design of feedback controllers for the maneuvers/vibration arrests of flexible structures. The algorithm developed is shown to be equally useful in sequential or simultaneous design iterations that modify the structural parameters, sensor/actuator locations, and control feedback gains. The approach is demonstrated using a differential equation model for the 'Draper /RPL configuration'. This model corresponds to the hardware used for experimental verification of large flexible spacecraft maneuver controls. A number of sensor/actuator configurations are studied vis-a-vis the degree of controllability. Linear output feedback gains are determined using a novel optimization strategy. The feasibility of the approach is established, but more research and numerical studies are required to extend these ideas to truly high-dimensioned systems. (Author)
Nature inspireda* optimization algorithms, namely artificial bee colony (ABC) optimization and firefly algorithm (FA), have been applied to synthesize beam patterns of a hexagonal planar array of isotropic elements. Two...
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Nature inspireda* optimization algorithms, namely artificial bee colony (ABC) optimization and firefly algorithm (FA), have been applied to synthesize beam patterns of a hexagonal planar array of isotropic elements. Two different cases, comprising two different beam patterns of a pencil beam and a square footprint pattern over a bounded region with lower peak sidelobe levels are presented. The pencil beam is generated by thinning the uniformly excited array and the square footprint pattern is generated by imposing optimum amplitudes, phases, and their corresponding states ("on"/"off") to the array elements. The optimum values of the parameters for both the cases are computed using ABC and FA individually, and the superiority of FA over ABC for the proposed problem in terms of computing solutions for both the cases is established.
The genetic algorithm (GA), particle swarm optimization (PSO) algorithm, and BOX algorithm have been used in natural gas liquefaction process optimization. Three algorithms all can find a solution by adopting differen...
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The genetic algorithm (GA), particle swarm optimization (PSO) algorithm, and BOX algorithm have been used in natural gas liquefaction process optimization. Three algorithms all can find a solution by adopting different strategies and computational efforts. Therefore, it is necessary to compare their performance. This article presents a performance comparison of the GA, PSO, and BOX algorithms for the optimization of four natural gas mixed refrigerant liquefaction processes. The results show that PSO has the best optimization performance, reducing the specific energy consumption (SEC) to 0.3233 kWh/kg, 0.2351 kWh/kg, 0.2489 kWh/kg, and 0.2427 kWh/kg for single mixed refrigerant (SMR), dual mixed refrigerant (DMR), propane pre-cooling mixed refrigerant (C3MR), and mixed fluid cascade (MFC), respectively. Furthermore, PSO also improved the exergy efficiency of the four processes to 35.34%, 48.59%, 45.90%, and 47.07%. The composite curve analysis shows that the heat transfer efficiency of the heat exchanger optimized by PSO is more efficient. The study also discovered that the total optimization performance of PSO and GA is better than BOX algorithm, and the GA optimization performance is second only to PSO. This research would greatly assist process engineers in making the right decision on process optimization to overcome energy efficiency challenges. (c) 2022 The Authors. Published by Elsevier Ltd.
Compares threea* optimization algorithms and evaluates their performance when applied to airfoil design. Gradient-based method; Simulated annealing; Genetic algorithm; Airfoild design using approximation method.
Compares threea* optimization algorithms and evaluates their performance when applied to airfoil design. Gradient-based method; Simulated annealing; Genetic algorithm; Airfoild design using approximation method.
Green products are considered to be the only way for human beings to follow the strategy of sustainable development. They have become one of the hotspots of modern design, manufacture and consumption. A green design m...
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Green products are considered to be the only way for human beings to follow the strategy of sustainable development. They have become one of the hotspots of modern design, manufacture and consumption. A green design method of electromechanical products based on case-based reasoning is presented in this paper. This paper puts forward and uses a "EW&AHP fusion technology" to scientifically determine the index weight, and uses multi target decision-making method to design the index system, establish the evaluationa* optimization algorithm model of green electromechanical product design scheme, and comprehensively evaluate and optimize the green product design scheme from the aspects of economy, technology and green. Sort, provide decision support for production and operation of related enterprises. The results show that the algorithm can not only give full play to the role of the data itself, but also fully reflect the green requirements of the green electromechanical products, and also give consideration to the profit goal of the enterprise.
Characterizing the geometric imperfections of ultra-thin composite structures is important since imperfections create weak points where local buckling is likely to occur. This work develops a thorough method for measu...
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