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Inverse design of dual-bandpass metasurface filters empowered by the multi-objective genetic algorithm

OPTICS COMMUNICATIONS 2024年 566卷

作者： Yu, Ke Ge, Jiahao Li, Haonan Zhang, Yaqiang Dong, Hongxing Zhang, Long Shanghai Univ Sch Microelect Shanghai 201800 Peoples R China Chinese Acad Sci Shanghai Inst Opt & Fine Mech Key Lab Mat High Power Laser Shanghai 201800 Peoples R China Univ Chinese Acad Sci Ctr Mat Sci & Optoelect Engn Beijing 100049 Peoples R China Hangzhou Inst Opt & Fine Mech Hangzhou 311421 Peoples R China Univ Chinese Acad Sci Hangzhou Inst Adv Study Hangzhou 310024 Peoples R China

multi-band metasurface filters are becoming increasingly pivotal in high-capacity communication technologies. Traditional methods for designing metasurface structures, to date, have relied on empirical approaches to obtain target electromagnetic responses, thereby suffering from low efficiency. Here, we demonstrate an advanced approach for the inverse design of a multi-band metasurface filter, which consists of a multi-objective genetic algorithm (MOGA) in tandem with equivalent circuit model (ECM) analysis. This integration converts specific frequency response requirements into ECM parameter constraints, significantly streamlining the metasurface design and optimization process and offering superior solutions. Using this inverse design method, we theoretically propose a dual-bandpass metasurface filter which can exhibit transmission passbands in any two adjacent frequency ranges among the X-, Ku-, and K-bands. Further, numerical simulations validate the performances of the proposed device, which show great agreement with MOGA-based predictions. Our results pave the way to the effective inverse design of multi-passband metasurface filters which are useful in many applications, such as microwave filters, radar and satellite communication systems, and radio frequency identification devices.

关键词： Metasurface Bandpass filters multi-objective genetic algorithm Equivalent circuit model

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A multi-objective genetic algorithm approach applied to compact meander branch line couplers design for 5G-enabled IoT applications

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JOURNAL OF COMPUTATIONAL ELECTRONICS 2024年第3期23卷 634-646页

作者： Abouhssous, Khadija Wakrim, Layla Zugari, Asmaa Zakriti, Alia Abdelmalek Essaadi Univ Natl Sch Appl Sci Dept Civil & Ind Sci & Technol Lab Sci & Adv Technol Tetouan Morocco Higher Inst Engn & Business ISGA Lab Innovat Management & Engn Business LIMIE Marrakech Morocco Abdelmalek Essaadi Univ Informat & Telecommun Syst Lab FS Tetouan Morocco

This article describes a multi-objective genetic algorithm (MOGA)-based procedure used for the size reduction of a hybrid compact branch line coupler (BLC) intended for 5G applications that meet the requirements of IoT applications. Conventional lambda/4 coupler transmission lines are replaced with meandering transmission lines to provide three different, simple and elegant designs that can operate at 3.5 GHz. A MOGA process is used to simultaneously balance the different design requirements and significantly reduce the bulky conventional structure size while maintaining high performance. To implement the optimization process, the proposed BLCs are designed using an interface between MATLAB software and a VBA script in the CST Studio simulator. The simulation results demonstrate a size reduction of 73.11%, 76.2% and 80%, respectively, for the three designs compared to conventional one. Then, for the demonstration of miniature BLCs operating at 3.5 GHz are fabricated on an FR-4 substrate. The measurements show good agreement with those obtained by simulation, making these BLCs a suitable choice for modern telecommunication systems requiring high compactness.

关键词： multi-objective genetic algorithm Branch line coupler Meander line 5G applications IoT applications

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Determining optimal controls placed on injection/production wells during waterflooding in heterogeneous oil reservoirs using artificial neural network models and multi-objective genetic algorithm

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COMPUTATIONAL GEOSCIENCES 2024年第6期28卷 991-1013页

作者： Nwanwe, Onyebuchi Ivan Izuwa, Nkemakolam Chinedu Ohia, Nnaemeka Princewill Kerunwa, Anthony Uwaezuoke, Nnaemeka Fed Univ Technol Owerri Dept Petr Engn Owerri Imo Nigeria

The objective of this study is to propose a computationally inexpensive and effective approach that addresses the challenges faced with computationally expensive and time-consuming trial-and-error and direct optimization methods in well-control optimization. This approach involves combining proxy models such as artificial neural network (ANN) models with optimization algorithms to determine an optimal solution much faster. It was implemented in a heterogeneous oil reservoir undergoing waterflooding. The controllable parameters of the reservoir simulation model were identified as bottom-hole pressure for the producers and water injection rate for the injectors. Minimum and maximum values of each input parameter were defined based on reservoir conditions and used with a Box Behnken design (BBD) method to generate realizations for conducting reservoir simulations to obtain cumulative oil produced (COP) and cumulative water produced (CWP). The input and output data were normalized before being used for model development such that 70:15:15% of data was used for training, validation, testing, and all of the ANN model in which a coefficient of correlation (R) of 0.99756, 0.94354, 0.95813, and 0.98589 were obtained respectively. This indicates the accuracy, validity, and reliability of the model. The coefficient of determination (R2) for training, validation, testing, and all datasets as well as statistical error and trend analysis were used to validate the model. R2 values for each case were not less than 0.80, and the responses were reproduced by the ANN model with average relative error and root mean square error of not more than 0.7%. Weights and biases were extracted from the trained and validated ANN model to aid in outputting a visible ANN model that can be used for optimization studies. A multi-objective genetic algorithm was used to determine an optimal solution that maximized COP and minimized CWP. Average and optimized input data were used to run the developed AN

关键词： Waterflooding Design of experiments Box Behnken design method Reservoir simulation MATLAB Artificial neural network Well control optimization multi-objective genetic algorithm

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Investigation and optimization of polyolefin elastomers polymerization processes using multi- objective genetic algorithm

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CHEMICAL ENGINEERING RESEARCH & DESIGN 2023年第1期193卷 383-393页

作者： Ruan, Shi-Xiang Zhang, Xi-Bao Luo, Zheng-Hong Shanghai Jiao Tong Univ Sch Chem & Chem Engn Dept Chem Engn State Key Lab Met Matrix Composites Shanghai 200240 Peoples R China

The aim of this research is to develop continuous reactor network models to determine the reactor configuration and corresponding optimal operating conditions of a polyolefin elastomers polymerization process. To achieve this objective, a modified vapor-liquid equilibrium model is integrated with both the continuous stirred tank reactor (CSTR) model and the plug flow reactor (PFR) model to accurately predict the vaporization rates of liquid. The multi-objective problem is formulated to maximize the monomer conversion and minimize the total annualized cost simultaneously. To solve the multi-objective problem, the genetic algorithm is utilized to identify the optimal operating conditions. Furthermore, the impact of the reactor combination mode is analyzed by varying the number and sequence of CSTR or PFR. The research findings reveal that a multi-phase CSTR without circulating stream followed by a PFR is the optimal reactor configuration. The proposed configuration can enhance reactor productivity and reduce the total an-nualized cost simultaneously while meeting the desired polymer properties.(c) 2023 Institution of Chemical Engineers. Published by Elsevier Ltd. All rights reserved.

关键词： multi-objective optimization multi-objective genetic algorithm Polyolefin elastomers polymerization Reactor configuration Operating conditions

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Optimization on high-pressure side of pump-turbine runner based on high efficiency and stability criterion via multi-objective genetic algorithm method

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART A-JOURNAL OF POWER AND ENERGY 2023年第8期237卷 1743-1761页

作者： Qin, Yonglin Li, Deyou Wang, Hongjie Liu, Zhansheng Wei, Xianzhu Wang, Xiaohang Song, Yongjian Harbin Inst Technol Sch Energy Sci & Engn 92 West Dazhi St Harbin 150001 Peoples R China Harbin Inst Large Elect Machinery State Key Lab Hydropower Equipment Harbin 150040 Peoples R China

Pumped storage power plants are doomed to play a more important role in peak-valley shifting hence the demand for operation stability is gradually regarded as an equal important criterion as high efficiency. In our paper, new concepts, namely "swept," "bowed (lean)," and "twisted" are introduced to systematic innovative design the geometry of high-pressure side (HPS) of a pump-turbine runner. Thereafter, a multi-objective optimization process, which consists of design of experiment (DoE), meta model generation, multi-objective genetic algorithm (MOGA), self-organization map (SOM) and takes both high efficiency and stability discipline into account is produced. The final optimization plan is selected and testified numerically. Compared to the original runner, the efficiency is improved from 91.7% to 91.9% at pump mode and improved from 92.3% to 92.8% at turbine mode. Moreover, the S margin is increased from 89.34 degrees to 90.23 degrees at small GVO and increased from 79.56 degrees to 80.29 degrees at large GVO. Besides, hump unsteady region is completely eliminated. Moreover, the flow characteristic comparation is conducted based on local hydraulic loss rate (LHLR) method. For design points, the optimized runner obviously decreases the hydraulic loss in hub and middle part of runner domain for turbine mode and slightly decrease the hydraulic loss in stay vane region for pump mode. For unsteady characteristics, HPS can better adjust the hydraulic loss distribution in corresponding discharge operating points, largely decreasing S unsteady characteristic in turbine mode and eliminating hump unsteady characteristic in pump mode. We believe that the methods proposed in our paper can bring the design of hydraulic machinery to a new level.

关键词： Pump turbine high pressure side multi-objective genetic algorithm hump characteristic S characteristic

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Design of S-band photoinjector with high bunch charge and low emittance based on multi-objective genetic algorithm

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Nuclear Science and Techniques 2023年第3期34卷 93-105页

作者： Ze-Yi Dai Yuan-Cun Nie Zi Hui Lan-Xin Liu Zi-Shuo Liu Jian-Hua Zhong Jia-Bao Guan Ji-Ke Wang Yuan Chen Ye Zou Hao-Hu Li Jian-Hua He The Institute for Advanced Studies Wuhan UniversityWuhan 430072China

High-brightness electron beams are required to drive LINAC-based free-electron lasers(FELs)and storage-ring-based synchrotron radiation light *** bunch charge and RMS bunch length at the exit of the LINAC play a crucial role in the peak current;the minimum transverse emittance is mainly determined by the injector of the ***,a photoin-jector with a high bunch charge and low emittance that can simultaneously provide high-quality beams for 4th generation synchrotron radiation sources and FELs is *** design of a 1.6-cell S-band 2998-MHz RF gun and beam dynamics optimization of a relevant beamline are presented in this *** dynamics simulations were performed by combining ASTRA and the multi-objective genetic algorithm NSGA *** effects of the laser pulse shape,half-cell length of the RF gun,and RF parameters on the output beam quality were analyzed and *** normalized transverse emittance was optimized to be as low as 0.65 and 0.92 mm·mrad when the bunch charge was as high as 1 and 2 nC,***,the beam stability properties of the photoinjector,considering misalignment and RF jitter,were simulated and analyzed.

关键词： Electron linear accelerator Photoinjector Beam dynamics multi-objective genetic algorithm

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Dynamic programming method of sustainable land use based on multi-objective genetic algorithm

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INTERNATIONAL JOURNAL OF ENVIRONMENTAL TECHNOLOGY AND MANAGEMENT 2024年第4-6期27卷 356-369页

作者： Zhou, Wuyi ChangChun Univ Architecture & Civil Engn Sch Publ Art Changchun 130000 Peoples R China

The traditional dynamic programming method of sustainable land use has some problems, such as low ecological benefit coefficient and large error in calculating the economic value of planning results. Therefore, a dynamic programming method of sustainable land use based on multi-objective genetic algorithm is proposed. Firstly, the population discrete model is used to deeply predict the per capita land use demand and determine the multi-objective object of dynamic programming. Secondly, using the grey objective programming method, the planning objective functions are mainly determined to maximise economic benefits, environmental benefits, ecological benefits, and social benefits. Finally, the multi-objective genetic algorithm is used to solve the objective function, and the results are used for dynamic programming of sustainable land use. The experimental results show that the ecological benefit coefficient after the proposed method of sustainable land dynamic programming is close to 1, and the error of economic value calculation is reduced.

关键词： multi-objective genetic algorithm land use dynamic programming population discrete model grey objective objective function

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Vibration Analysis of Switched Reluctance Motor Based on multi-objective genetic algorithm

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2023年第2期18卷 1003-1011页

作者： Huang, Xiaocun Rao, Yingying Jing, Libing Cangzhou Normal Univ Coll Phys & Informat Engn 16 Guofeng South Ave Cangzhou Hebei Peoples R China China Three Gorges Univ Coll Elect Engn & New Energy Daxue Rd 8 Yichang Hubei Peoples R China

The vibration and noise caused by radial electromagnetic force hinder the development of switched reluctance motor (SRM). A structure with auxiliary slots in the stator teeth and rotor teeth of SRM is proposed in this paper. The radial force density and average torque are optimized by multi-objective genetic algorithm, and the optimal structural parameters of the motor are determined. The modal analysis and harmonic response analysis of the stator are carried out by using Workbench finite element software and compared with the conventional SRM. The results show that the radial force density is reduced by 42.19%, which verifies that opening auxiliary slots in stator teeth and rotor teeth is conducive to reducing the vibration of SRM.

关键词： Switched reluctance motor multi-objective genetic algorithm Modal analysis Harmonic response analysis

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A combination of multi-objective genetic algorithm and deep learning for music harmony generation

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multiMEDIA TOOLS AND APPLICATIONS 2023年第2期82卷 2419-2435页

作者： Majidi, Maryam Toroghi, Rahil Mahdian Iran Broadcasting Univ Fac Media Technol & Engn Tehran Iran

Automatic Music Generation (AMG) has become an interesting research topic for many scientists in artificial intelligence, who are also interested in the music industry. One of the main challenges in Automatic Music Generation is that there is no clear objective evaluation criterion that can measure the music grammar, structural rules, and audience satisfaction. Also, original music contains different elements that should work together, such as melody, harmony, and rhythm;but in the most of previous works, Automatic Music Generation works only for one element (e.g., melody). Therefore, in this paper, we propose a multi-objective genetic algorithm (MO-GA) to generate polyphonic music pieces, considering grammar and listener satisfaction. In this method, we use three objective functions. The first objective function is the accuracy of the generated music piece, based on music theory;and the other two objective functions are modeled scores provided by music experts and ordinary listeners. The scoring of experts and listeners separately are modeled using Bi-directional Long Short-Term Memory (Bi-LSTM) neural networks. The proposed music generation system tries to maximize mentioned objective functions to generate a new piece of music, including melody and harmony. The results show that the proposed method can generate pleasant pieces with desired styles and lengths, along with harmonic sounds that follow the grammar.

关键词： Automatic Music generation Polyphonic Music pieces Harmony multi-objective genetic algorithm Bi-LSTM

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Energy audit and management of environmental GHG emissions based on multi-objective genetic algorithm and data envelopment analysis: An agriculture case

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ENERGY REPORTS 2023年 10卷 1507-1520页

作者： Taherzadeh-Shalmaei, Nahid Rafiee, Mahta Kaab, Ali Khanali, Majid Rad, Mohammad Amin Vaziri Kasaeian, Alibakhsh Univ Tehran Coll Agr Dept Agr Machinery Engn Karaj Iran Univ Tehran Fac New Sci & Technol Dept Renewable Energies & Environm Tehran Iran

This study aimed to address the challenges of energy consumption and environmental emissions in the mushroom production sector through an energy audit analysis and life cycle assessment approach. The main objective of the study was to estimate energy and environmental indicators, optimize their outputs using multi-objective genetic algorithm and data envelopment analysis methods, and identify opportunities for energy savings in the mushroom production process. The energy flow analysis revealed that the total input energy for mushroom production was 1022537.82 MJ/m2, while the total output energy was only 11125.94 MJ/m2, resulting in an energy use efficiency rate of 0.01, indicating significant energy imbalance and inefficiency. Electrical energy consumption had the largest share of total consumed energy, approximately 80.6%. The life cycle assessment results showed that the mushroom production chain emits about 8.50 x 10+3 kg of CO2 GHG. The results of energy optimization demonstrate that between 6.5% and 25% reductions in energy utilization can be achieved during mushroom production. Among the input energies, compost had the largest quota in energy savings of almost 7% and 26% by data envelopment analysis and multi-objective genetic algorithm techniques, respectively. Thus, it is recommended to implement the multi-objective genetic algorithm technique to identify opportunities for energy savings and reduce environmental emissions in the mushroom production sector, which can lead to significant energy savings and contribute to environmental sustainability while reducing operating costs.& COPY;2023 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Energy audit Environmental sustainability Optimization multi-objective genetic algorithm

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