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20th IEEE International Conference on Electrical Machines and Systems (ICEMS)

作者： Shao, Xinxin Naghdy, Fazel Du, Haiping Univ Wollongong Sch Elect Comp & Telecommun Engn Northfields Ave Wollongong NSW 2522 Australia

ISBN: (纸本)9781538632468

Parameter optimization of active suspension in in-wheel motor driven electric vehicle using genetic algorithm (GA) is presented. In such vehicles, placing the motors in the wheel results in an increase in the unsprung mass, which greatly deteriorates the suspension ride comfort performance and road holding ability. Structures with suspended shaftless direct-drive motors have the potential to improve the road holding capability and ride performance. The GA is applied to obtain the optimal parameters under different road profiles. Parameters of the motor suspension (damping and stiffness coefficients), vehicle suspension and active controller are optimized based on quarter vehicle model. The optimization process aims to minimize the vertical acceleration of sprung mass and motor, dynamic force transmitted to the motor as well as suspension working space and road holding capability. The performance of the vehicle with passive suspension, active suspension with unoptimized parameters and optimized parameters are compared. The results show that active suspension with optimized parameters significantly outperforms other suspensions in motor ride performance.

关键词： genetic algorithm optimization motor parameters optimization ride performance

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Improved CNFET Performance Based on genetic algorithm Parameters optimization 8

Improved CNFET Performance Based on Genetic Algorithm Parame...

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8th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)

作者： Sayed, Shimaa. I. Abutaleb, M. M. Nossair, Zaki B. Helwan Univ Elect Commun & Comp Engn Cairo Egypt

ISBN: (纸本)9781538633717

The performance of Carbon Nanotube Field Effect Transistors (CNFETs) depends critically on device parameters such as CNT diameter, number of nanotubes, and inter-nanotube spacing. To achieve a minimum Power-Delay Product (PDP) of the CNFET based digital design, the genetic algorithm (GA) is used in this paper to optimize CNFET device parameters. The results of GA optimization are found to be 10 carbon nanotubes with 1.33nm CNT diameter and 4nm spacing between them. The performance of CNFET digital circuits (such as XOR2, 2x1 MUX and D-latch) based on these optimum values is compared with that designed based on typical parameters. The simulation results demonstrate better performance of proposed CNFET based circuits with the significant reduction of PDP values in comparison with previous designs.

关键词： CNFET device parameters genetic algorithm optimization power-delay product minimization nanoelectronic circuit design

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Adaptive robust PID control subject to supervisory decoupled sliding mode control based upon genetic algorithm optimization

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2015年第4期37卷 505-514页

作者： Mahmoodabadi, M. J. Taherkhorsandi, M. Talebipour, M. Castillo-Villar, K. K. Sirjan Univ Technol Dept Mech Engn Sirjan Kerman Iran Univ Texas San Antonio Dept Mech Engn San Antonio TX 78249 USA

While proportional-integral-derivative (PID) control is the most applicable controller in industry, it does not provide important qualities, such as stability in case of uncertainties, being robust to disturbances, and optimal control inputs. To address the issue, this paper presents adaptive robust PID control subject to supervisory decoupled sliding mode control for an inverted pendulum system optimized by using a genetic algorithm optimization. Decoupled sliding mode control is a variable structure control method having appropriate features, such as good tracking performance and robustness with regard to disturbances. To this end, decoupled sliding mode control as a supervisory controller is utilized in accordance with PID control to deliver necessary control inputs and enhance the performance of the controller. Effectual methods such as the transfer function which resulted in having minimum chattering in the controller is employed in this paper. An adaptation mechanism is used to update the proportional, derivative, and integral gains of PID control. Then, the parameters of the controller are ascertained by using a genetic algorithm. The results and analysis prove the proper performance of the controller via providing an optimal smooth control input, proper tracking performance, and the elimination of the chattering problem.

关键词： Adaptive robust control decoupled sliding mode control genetic algorithm optimization PID control the inverted pendulum and cart system

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Design of metaporous supercells by genetic algorithm for absorption optimization on a wide frequency band

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APPLIED ACOUSTICS 2016年 102卷 49-54页

作者： Lagarrigue, C. Groby, J. -P. Daze, O. Tournat, V. Univ Maine LUNAM Univ CNRS LAUM UMR 6613 F-72085 Le Mans France

The optimization of acoustic absorption by metaporous materials made of complex unit cells with 2D resonant inclusions is realized using genetic algorithm. A nearly total absorption over a wide frequency band can be obtained for thin structures, even for frequencies below the quarter wavelength resonances i.e., in a sub-wavelength regime. The high absorption performances of this material are due to the interplay of usual visco-thermal losses, local resonances and trapped modes. The density of resonant and trapped modes in this dissipative porous layer, is a key parameter for broadband absorption. The best configurations and critical coupling conditions are found by genetic algorithm optimization. Several types of resonators are included gradually in the studied configurations (split-rings, Helmholtz resonators, back cavities) with increasing complexity. The optimization leads to a metaporous structure with a 2-cm sub-wavelength layer thickness, exhibiting a nearly total absorption between 1800 Hz and 7000 Hz. The influence of the incidence angle on the absorption properties is also shown. (C) 2015 Published by Elsevier Ltd.

关键词： Metamaterials Porous materials Metaporous Finite element method genetic algorithm optimization

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Production Scheduling using Augmented Lagrangian Relaxation and genetic algorithm optimization 24

Production Scheduling using Augmented Lagrangian Relaxation ...

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24th INTERNATIONAL MINING CONGRESS OF TURKEY

作者： Moosavi, E. Gholamnejad, J. Ataee-pour, M. Khorram, E. Islamic Azad Univ South Tehran Branch Dept Min Engn Tehran Iran Yazd Univ Dept Min & Met Engn Yazd Iran Amirkabir Univ Technol Dept Min & Met Engn Tehran Iran Amirkabir Univ Technol Dept Appl Math & Comp Sci Tehran Iran

ISBN: (纸本)9786050107050

To improve the quality of decision making in the mining operation, it is essential to find global optimum in problems with large dimensional scales. It is widely accepted that Long-term production scheduling (LTPS) problem is playing key role in mining projects to improve their performance by considering availability constraints while maximizing the project total profits during the period. Since production scheduling problems are NP-hard, there is need of improving scheduling methodologies to get good solution. This paper presents a hybrid model between augmented Lagrangian relaxation (ALR) and genetic algorithm (GA) to solve the LTPS problem. We propose to apply the ALR method on the LTPS problem which to improve its performance speeding up the convergence and also, GA is used to update the Lagrangian multipliers. The results from synthetic data set show that the ALR method is effective in solving large-scale problem and generation a feasible solution then the traditional linearization method. Furthermore, the proposed hybrid strategy based on GA showed better performance in comparison to the available methods.

关键词： Augmented Lagrangian Relaxation genetic algorithm optimization

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Task-Offloading optimization Using a genetic algorithm in Hybrid Fog Computing for the Internet of Drones

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SENSORS 2025年第5期25卷 1383-1383页

作者： Attalah, Mohamed Amine Zaidi, Sofiane Mellal, Nacima Calafate, Carlos T. Univ Ctr Tipaza Dept Elect Tipasa 42000 Algeria Univ Oum Bouaghi Dept Math & Comp Sci Res Lab Comp Sci Complex Syst RELA CS 2 Oum El Bouaghi 04000 Algeria Univ Oum El Bouaghi Dept Networks & Telecommun Res Lab Artificial Intelligence & Autonomous Objec Oum El Bouaghi 04000 Algeria Univ Politecn Valencia Dept Comp Engn DISCA Valencia 46022 Spain

Research and development on task offloading over the Internet of Drones (IoD) has expanded rapidly in the last few years. Task offloading in a fog IoD environment is very challenging due to the high dynamics of the IoD topology, which cause intermittent connections, as well as the stringent requirements of task offloading, such as reduced delay. To overcome these challenges, in this paper, we propose a task-offloading optimization strategy using a heuristic genetic algorithm (GA) with hybrid fog computing technology for the Internet of Drones, named GA Hybrid-Fog. The proposed solution employs a GA for task offloading from edge Unmanned Aerial Vehicles (UAVs) to both fog base stations (FBSs) and fog UAVs (FUAVs) in order to optimize offloading delays (transmission and fog computing delays) and guarantee higher storage and processing capacity. Experimental results show that GA Hybrid-Fog achieves greater improvements in task-offloading delays compared to other IoD technologies (GA BS-Fog, GA UAV-Fog, and GA UAV-Edge).

关键词： Internet of Drones fog computing networks genetic algorithm optimization task offloading in IoD unmanned aerial vehicles

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Performance assessment and optimization of water spray strategy for indirect evaporative cooler based on artificial neural network modeling and genetic algorithm

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APPLIED ENERGY 2024年 368卷

作者： Ma, Xiaochen Shi, Wenchao Lu, Lin Yang, Hongxing Hong Kong Polytech Univ Dept Bldg Environm & Energy Engn Renewable Energy Res Grp RERG Hong Kong Peoples R China

Indirect evaporative cooling has garnered considerable attention and research owing to its notable advantages, especially high cooling efficiency and low energy consumption. However, the complexity and diversity of parameters affecting indirect evaporative cooler (IEC) performance necessitate the development of a fast and efficient performance prediction model, which remains a critical research objective. To address the particular challenge, this paper introduces a novel approach that utilizes the principles of neural network theory to construct a predictive model based on backpropagation artificial neural networks (BP-ANN). The developed model is employed to investigate the impact of six main operating parameters of the water spray system on the IEC performance. Wet-bulb efficiency and pressure drop of secondary air channel are utilized as the performance evaluation indexes. The model is rigorously validated by relevant experimental data, enabling a comprehensive analysis of the influence of each parameter on the operational performance of the whole system. The significance of the influential parameters is evaluated through grey relational analysis. Furthermore, a multi-objective optimization approach employing a genetic algorithm (GA) is introduced to effectively determine the optimal operating parameters of the IEC system. The results demonstrate that the whole system can efficiently operate with low energy consumption when the wet-bulb efficiency and pressure drop weights are balanced. The optimal operating parameters include a nozzle pressure of 0.15 MPa, spacing of 85 mm, aperture of 6 mm, count of 11, spray density of 0.85 kg/(s center dot m2) center dot m 2 ) and air velocity of 2 m/s, at which time the wet-bulb efficiency was 0.85, the pressure drop was 210 Pa, and the coefficient of performance (COP) was 12.4, which demonstrated an efficiency increase of 23.2% in comparison with the base system. The findings of this research significantly contribute to a c

关键词： Indirect evaporative cooler Artificial neural network Performance prediction Grey relational analysis genetic algorithm optimization

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Gas exchange optimization in aircraft engines using sustainable aviation fuel:A design of experiment and genetic algorithm approach

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Energy and AI 2024年第3期17卷 261-280页

作者： Zheng Xu Jinze Pei Shuiting Ding Longfei Chen Shuai Zhao Xiaowei Shen Kun Zhu Longtao Shao Zhiming Zhong Huansong Yan Farong Du Xueyu Li Pengfei Yang Shenghui Zhong Yu Zhou Hangzhou International Innovation Institute Beihang UniversityHangzhou311115China Research Institute of Aero-Engine Beihang UniversityBeijing102206China Civil Aviation University of China Tianjin300300China Aircraft/Engine Integrated System Safety Beijing Key Laboratory Beijing100083China Aero Engine Academy of China Beijing101304China School of Energy and Power Engineering Beihang UniversityBeijing100083China AVIC Chengdu Aircraft Design and Research Institute Chengdu610091China AVIC Nanjing Engineering Institute of Aircraft Systems Nanjing211106China School of Transportation Science and Engineering Beihang UniversityBeijing100083China Faculty of Science and Engineering University of Nottingham Ningbo ChinaNingbo315100China

The poppet valves two-stroke(PV2S)aircraft engine fueled with sustainable aviation fuel is a promising option for general aviation and unmanned aerial vehicle propulsion due to its high power-to-weight ratio,uniform torque output,and flexible valve ***,its high-altitude gas exchange performance remains unexplored,presenting new opportunities for optimization through artificial intelligence(AI)*** study uses validated 1D+3D models to evaluate the high-altitude gas exchange performance of PV2S aircraft *** valve timings of the PV2S engine exhibit considerable flexibility,thus the Latin hypercube design of experiments(DoE)methodology is employed to fit a response surface model.A genetic algorithm(GA)is applied to iteratively optimize valve timings for varying *** optimization process reveals that increasing the intake duration while decreasing the exhaust duration and valve overlap angles can significantly enhance high-altitude gas exchange *** optimal valve overlap angle emerged as 93°CA at sea level and 82°CA at 4000 m *** effects of operating parameters,including engine speed,load,and exhaust back pressure,on the gas exchange process at varying altitudes are further *** higher engine speed increases trapping efficiency but decreases the delivery ratio and charging efficiency at various *** effect is especially pronounced at elevated *** increase in exhaust back pressure will significantly reduce the delivery ratio and increase the trapping *** study demonstrates that integrating DoE with AI algorithms can enhance the high-altitude performance of aircraft engines,serving as a valuable reference for further optimization efforts.

关键词： Poppet valves two-stroke Design of experiment genetic algorithm optimization Heavy fuel aircraft engine High altitude gas exchange performance

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Multi-objective optimization on bionic fractal structure for heat exchanging of single fluids by genetic algorithm

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INTERNATIONAL JOURNAL OF HEAT AND FLUID FLOW 2024年 110卷

作者： Yao, Zikang Pan, Lisheng Mu, Bai Wei, Xiaolin Chinese Acad Sci State Key Lab High Temp Gas Dynam Inst Mech Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Engn Sci Beijing 100049 Peoples R China Beijing Univ Civil Engn & Architecture Sch Environm & Energy Engn Beijing 100044 Peoples R China

The heat transfer process using a single-fluid cooling wall is widely used in various fields, including highperformance computers, solar panels, automotive power supplies, and aerospace engine protection. In high heat flux environments, thermal management has become a significant challenge, and enhancing heat transfer is crucial for effective thermal management. Although parallel microchannel heat transfer structures are a current research focus due to their compact structure and easy manufacturing, their uneven flow distribution is a key constraint hindering further development. Bionic fractal heat transfer structures have gained widespread attention due to their favorable flow uniformity, high efficiency, and low energy consumption. Currently, the structural design of biomimetic fractal heat transfer structures mostly employs Murray's law and its derived parameters. A thermal design program suitable for variable flow rate and physical properties is proposed and a model for a single fluid biomimetic fractal heat transfer structure is established. The heat transfer characteristics of fractal heat transfer structures were explained using supercritical CO2 as a heat transfer medium. The study comprehensively analyzed the influence of different design parameters of single-fluid biomimetic fractal heat transfer structures on heat transfer performance and provided a method for optimizing their design. The results showed that there exists an optimal value for heat transfer performance indicators under different structural design parameters. genetic algorithms are applied to multi-objective optimize the structural parameters of biomimetic fractal heat transfer structures and the performance indicator is improved by 21.6%.

关键词： Bionic fractal structure Thermo-hydraulic performance Heat transfer enhancement genetic algorithm optimization Single-fluid cooling

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optimization of GFRP-concrete-steel composite column based on genetic algorithm- artificial neural network

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APPLIED OCEAN RESEARCH 2024年 143卷

作者： Zhao, Zhongwei Bao, Yuyang Gao, Tian An, Qi Liaoning Tech Univ Sch Civil Engn Fuxin 123000 Peoples R China Chongqing Jiaotong Univ State Key Lab Mt Bridge & Tunnel Engn Chongqing 400074 Peoples R China Qingdao Univ Technol Sch Civil Engn Qingdao 266520 Peoples R China

genetic algorithms (GA) and artificial neural networks (ANN) as intelligent algorithms have been widely applied in engineering fields such as structural design. The GFRP-concrete-steel composite column (GCS), known for its corrosion resistance, has garnered significant attention in its design. However, the application of GA and ANN methods in the context of GCS has primarily been confined to enhancing the predictive accuracy of ANN. This paper introduces a novel approach for the structural optimization of GCS using GA and ANN. A total of 1050 finite element models were generated and analyzed using ANSYS for the purpose of training ANN. The validity of these finite element models was confirmed through comparative analysis with experimental data. The trained ANN was then utilized to predict the loading capacity of GCS for each generation in the GA. By collecting pricing data for GFRP tubes, steel tubes, and concrete under various parameters, pricing fitting formulas for the three materials were derived. Subsequently, the cost of GCS under different parameter sets was calculated. The maximization of the ratio of GCS loading capacity to its cost (LP) was established as the optimization objective. Through the iterative optimization process of the GA-ANN algorithm, the structural optimization was achieved to maximize the economic efficiency of GCS. Comparing the optimization results with numerical simulation results showed that errors were mostly contained within 10 % or even as low as 5 %, thereby validating the accuracy of the GA-ANN algorithm. The applicability of the GA-ANN algorithm to GCS structural optimization was demonstrated by altering a single concrete variable. The method proposed in this paper can be effectively applied to the structural design and optimization of composite columns with a sandwich structure, such as GCS.

关键词： Glass fiber-reinforced plastic Compressive strength Artificial neural network genetic algorithm optimization

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