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An Evaluation ofa* optimization algorithms for the Optimal Selection of GNSS Satellite Subsets

REMOTE SENSING 2024年第10期16卷 1794-1794页

作者： Alluhaybi, Abdulaziz Psimoulis, Panos Remenyte-Prescott, Rasa Univ Nottingham Nottingham Geospatial Inst Nottingham NG7 2TU England Taibah Univ Dept Civil Engn Madinah 42353 Saudi Arabia Univ Nottingham Dept Civil Engn Nottingham NG7 2RD England

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

关键词： GNSS GPS satellite selection WGDOP CNR-WGDOP optimization algorithm ABC ACO GA PSO SA

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Application ofa* optimization algorithms to adaptive motion control for repetitive process

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ISA TRANSACTIONS 2021年 115卷 192-205页

作者： Szczepanski, Rafal Tarczewski, Tomasz Grzesiak, Lech M. Nicolaus Copernicus Univ Dept Automat & Measurement Syst Grudziadzka 5 PL-87100 Torun Poland Warsaw Univ Technol Inst Control & Ind Elect Koszykowa 75 PL-00662 Warsaw Poland

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.

关键词： Adaptive controller optimization algorithm Repetitive process State feedback controller PMSM

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Metamodel-Based Hyperparameter optimization ofa* optimization algorithms in Building Energy optimization

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BUILDINGS 2023年第1期13卷 167页

作者： Si, Binghui Liu, Feng Li, Yanxia Nanjing Tech Univ Coll Architecture Nanjing 210044 Peoples R China Southeast Univ Sch Architecture Nanjing 210096 Peoples R China

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.

关键词： building energy efficiency building energy optimization hyperparameter optimization optimization algorithm ANN-trained metamodel

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EIGENVALUEa* optimization algorithmS FOR STRUCTURE CONTROLLER-DESIGN ITERATIONS

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 1985年第6期8卷 697-706页

作者： BODDEN, DS JUNKINS, JL TEXAS A&M UNIV DEPT AEROSP ENGNCOLLEGE STNTX 77843

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)

关键词： Eigenvalues optimization algorithm Actuators Sensors Feedback Control Flexible Spacecrafts Flexible Structures Closed Loop System Nonlinear Programming Structural Damping

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Synthesis of hexagonal planar array using swarm-baseda* optimization algorithms

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INTERNATIONAL JOURNAL OF MICROWAVE AND WIRELESS TECHNOLOGIES 2015年第2期7卷 151-160页

作者： Chatterjee, Anirban Mandal, Debasis Natl Inst Technol Goa Dept Elect & Commun Engn Ponda Goa India Bengal Coll Engn & Technol Dept Elect & Commun Engn Durgapur India

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.

关键词： optimization algorithm Artificial bee colony (ABC) algorithm Firefly algorithm (FA) Hexagonal planar array Sidelobe level (SLL) Square footprint Thinning

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Performance evaluation and comparison of commonly useda* optimization algorithms for natural gas liquefaction processes

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ENERGY REPORTS 2022年 8卷 4787-4800页

作者： Sun, Heng Geng, Jinliang Na, Fengyi Rong, Guangxin Wang, Chao China Univ Petr Natl Engn Lab Oil & Gas Pipeline Transmiss Safety Beijing Key Lab Urban Oil & Gas Transmiss & Distr Beijing 102249 Peoples R China

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.

关键词： Mixed refrigerant process Specific energy consumption optimization algorithm algorithm comparison

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Thermal Model Correlation of SNIPE Satellite Using Genetic-algorithm-Based Multi-Objective optimization Method

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JOURNAL OF SPACECRAFT AND ROCKETS 2023年第4期60卷 1333-1342页

作者： Kim, Ji-Seok Lee, Seonho Kim, Hui-Kyung Kim, Hae-Dong Korea Aerosp Res Inst Satellite Integrat & Test Ctr Daejeon 34133 South Korea Korea Aerosp Res Inst Div Satellite Space Explorat Syst Design Daejeon 34133 South Korea Gyeongsang Natl Univ Dept Aerosp & Software Engn Jinju 52828 South Korea

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Comparison ofa* optimization algorithms for aerodynamic shape design

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AIAA JOURNAL 1997年第8期35卷 1413-1415页

作者： Obayashi, S Tsukahara, T Tohoku University Sendai 980-77 Japan

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.

关键词： optimization algorithm Aerodynamic Performance Direct Numerical Simulation Laminar Boundary Layer Freestream Velocity Particle Image Velocimetry Search algorithm Convective Boundary Condition Navier Stokes Equations Energy Conservation

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Application of Decision Making algorithm for Green Electromechanical Product Design optimization

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JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2019年第2期23卷 293-299页

作者： Zhang, Yi Meng, Qinghui Henan Polytech Inst Sch Mech & Elect Engn 666 Kongming Rd Nanyang 473000 Henan Peoples R China Nanjing Univ Sci & Technol Sch Elect Engn & Optoelect Technol 200 Xiaolingwei Nanjing 210094 Jiangsu Peoples R China Henan Univ Sci & Technol Sch Control Engn 666 Kongming Rd Nanyang 473000 Henan Peoples R China

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.

关键词： green electromechanical products optimization algorithm entropy weight method analytic hierarchy process EW & AHP fusion technology

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Manufacturing Imperfections of Open Cross-Section Deployable Thin-Shell Composite Structures

Manufacturing Imperfections of Open Cross-Section Deployable...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Carmi, Meital O. Pellegrino, Sergio Graduate Aerospace Laboratories California Institute of Technology 1200 E California Blvd MC 105-50 PasadenaCA91125 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

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 measuring the geometric imperfections in thin-shell composite longerons. The mean geometrical parameters, the axial twist, and the lengths, amplitudes and locations of both local and periodic radial imperfections in experimental composite longerons are measured using this method. The results of this study identify the dominant imperfections commonly introduced during the manufacturing of these structures. This research was carried out with financial support from the National Science Foundation’s Graduate Research Fellowship Program and the Caltech Space Solar Power Project. The authors would like to acknowledge Professor Dan Meiron for his guidance on the wavelength decomposition used in this work. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Composite Structures Longerons Shape Functions Numerical Modeling Computing Autoclave Thin Shell Structure MATLAB optimization algorithm Structural Failure

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