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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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Extraction of battery parameters using a multi-objective genetic algorithm with a non-linear circuit model

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JOURNAL OF POWER SOURCES 2014年 259卷 76-86页

作者： Malik, Aimun Zhang, Zheming Agarwal, Ramesh K. Washington Univ Dept Mech Engn & Mat Sci St Louis MO 63130 USA

There is need for a battery model that can accurately describe the battery performance for an electrical system, such as the electric drive train of electric vehicles. In this paper, both linear and non-linear equivalent circuit models (ECM) are employed as a means of extracting the battery parameters that can be used to model the performance of a battery. The linear and non-linear equivalent circuit models differ in the numbers of capacitance and resistance;the non-linear model has an added circuit;however their numerical characteristics are equivalent. A multi-objective genetic algorithm is employed to accurately extract the values of the battery model parameters. The battery model parameters are obtained for several existing industrial batteries as well as for two recently proposed high performance batteries. Once the model parameters are optimally determined, the results demonstrate that both linear and non-linear equivalent circuit models can predict with acceptable accuracy the performance of various batteries of different sizes, characteristics, capacities, and materials. However, the comparisons of results with catalog and experimental data shows that the predictions of results using the non-linear equivalent circuit model are slightly better than those predicted by the linear model, calculating voltages that are closer to the manufacturers' values. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Battery performance Non-linear equivalent circuit model multi-objective genetic algorithm Parameter extraction

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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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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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A hybrid multi-objective genetic algorithm based on the ELECTRE method for a capacitated flexible job shop scheduling problem

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2015年第1-4期77卷 51-66页

作者： Rohaninejad, Mohamad Kheirkhah, Amirsaman Fattahi, Parviz Vahedi-Nouri, Behdin Bu Ali Sina Univ Dept Ind Engn Hamadan Iran

In this paper, a multi-objective flexible job shop scheduling problem with machines capacity constraints is studied. Minimizing the makespan and overtime costs of machines are considered as two objectives for evaluating solutions. First, a new nonlinear integer programming model is presented to formulate the problem. Inasmuch as this problem is well-known as a NP-hard problem, a hybrid metaheuristic algorithm (CFJSP II) is developed to overcome its complexity. Regarding to the solution space of the problem, for assigning and sequencing operations, a multi-objective genetic algorithm based on the ELECTRE method is presented. Also, a powerful heuristic approach to tradeoff the objective functions is developed. Finally, the proposed algorithm is compared with some well-known multi-objective algorithms such as NSGAII, SPEA2, and VEGA. Regarding to the computational results, it is clear that the proposed algorithm has a better performance especially in the closeness of the solutions to the Pareto optimal front.

关键词： Scheduling Flexible job shop multi-objective genetic algorithm ELECTRE method Pareto optimal front

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Minimizing total carbon footprint and total late work criterion in flexible job shop scheduling by using an improved multi-objective genetic algorithm

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RESOURCES CONSERVATION AND RECYCLING 2018年 128卷 267-283页

作者： Piroozfard, Hamed Wong, Kuan Yew Wong, Wai Peng Univ Teknol Malaysia Fac Mech Engn Dept Mfg & Ind Engn Utm Skudai 81310 Malaysia Univ Sains Malaysia Sch Management George Town 11800 Malaysia

In scheduling, previous research attention has been directed towards classical-based objective functions, while ignoring environmental-based objective functions. The purpose of this research is to present a multi-objective flexible job shop scheduling problem with the objectives of minimizing total carbon footprint and total late work criterion, simultaneously, as sustainability-based and classical-based objective functions, respectively. In order to solve the presented problem effectively, an improved multi-objective genetic algorithm is proposed to obtain high quality non-dominated schedules. This work has three main scientific contributions that are: (1) This is a novel and pioneer research that addresses carbon footprint reduction in flexible job shop scheduling, (2) This is also the first research that addresses the total late work criterion in multi-objective flexible job shop scheduling, and (3) This research proposes an improved multi-objective evolutionary algorithm for solving the newly extended bi-objective problem. Stepwise delineation of the proposed algorithm is provided and fifteen newly extended test instances are solved by the proposed approach. Computational outcomes of the proposed algorithm are compared with two most representative and well-known multi-objective evolutionary algorithms, namely, non-dominated sorting genetic algorithm II and strength Pareto evolutionary algorithm 2. The principal results show that: (1) The proposed algorithm is superior in finding high quality non-dominated schedules, (2) It performs better in four averaged comparison metrics as compared to the other algorithms, and (3) Carbon footprint has an impact on the optimum solutions. As conclusions, the proposed algorithm is useful for production managers to schedule their operations in a way that can reduce carbon emission while minimizing late work. Production managers will also have the flexibility in selecting a schedule from amongst a set of non-dominated schedules.

关键词： Bi-objective flexible job shop scheduling Environmentally sustainable operations management Carbon emission Late work criterion multi-objective genetic algorithm

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Parameters Optimization using Fuzzy Rule Based multi-objective genetic algorithm for an Event Based Rainfall-Runoff Model

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WATER RESOURCES MANAGEMENT 2018年第4期32卷 1501-1516页

作者： Reshma, T. Reddy, K. Venkata Pratap, Deva Agilan, V. Koneru Lakshmaiah Educ Fdn Dept Civil Engn Guntur 522502 Andhra Pradesh India Natl Inst Technol Dept Civil Engn Warangal 506004 Telangana India

The calibration of an event based rainfall-runoff model for steam flow forecasting is challenging because, it is difficult to measure the parameters physically on the field for each rainfall event. In the present study, Fuzzy rule based multi-objective genetic algorithm (MGA) is developed to optimize the infiltration and roughness parameters of an event based rainfall-runoff model. Nash Sutcliffe Efficiency (NSE), Coefficient of Determination (R-2) and transformed volume difference (f(V)) are used as the objective functions of the MGA and all Pareto optimal solutions are identified using Nondominated Sorting method. As three objective functions are included in the calibration, the number of Pareto optimal solutions are also increases and hence, the optimization problem now becomes a decision making problem. Therefore, to select the best solution from all Pareto optimal solutions, a Fuzzy Rule-Based Model (FRBM) is developed to get alternative values of each Pareto optimal solution. First, the Fuzzy rule based MGA is developed by integrating the FRBM with the MGA. Then the Fuzzy rule based MGA is integrated with an event based runoff model. The developed Fuzzy-MGA based runoff model is tested on three different watersheds and the simulation results of Fuzzy-MGA based runoff model are compared with observed data and previous study results. From the simulated events of three watersheds using Fuzzy-MGA based runoff model, it is observed that the mean percentage error in any criteria (i.e. volume of runoff, peak runoff, and time to peak) of the developed model for a watershed is less than 16.33%. It is also noted that the developed Fuzzy-MGA based runoff model is able to produce hydrographs that are much closer to the measured hydrographs.

关键词： Event based rainfall-runoff model Fuzzy Rule-Based Model multi-objective genetic algorithm Optimization

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Optimization of an aluminum alloy anti-collision side beam hot stamping process using a multi-objective genetic algorithm

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ARCHIVES OF CIVIL AND MECHANICAL ENGINEERING 2013年第3期13卷 401-411页

作者： Zhou, Jing Wang, Baoyu Lin, Jianguo Fu, Lei Univ Sci & Technol Beijing Sch Mech Engn Beijing 100083 Peoples R China Univ London Imperial Coll Sci Technol & Med Dept Mech Engn London SW7 2AZ England

The process parameters of aluminum alloy hot stamping are essential for product forming quality. In the case of an anti-collision side beam inside car doors, the finite-element model of aluminum alloy hot stamping is set up, and the forming quality is investigated under an ordinary process condition. The blank hold force (BHF) has a significant impact on the forming quality in hot stamping. Using the Latin hypercube method to sample the simulation data points and the finite-element (FE) model to calculate the forming quality indices of the data points according to the response value of the indices, the quadratic response surfaces between the process parameter inputs and the forming quality indices are initialized. Using the multi-objective genetic algorithm NSGA-II (non-dominated sorting genetic algorithm) to optimize the responses of the process parameters, the Pareto solutions corresponding to combinations of the blank hold force and stamping speed are obtained. Finally, based on the optimal process parameters, stamping tests are carried out. Compared with the results of the stamping trial and numerical simulation, it is demonstrated that the finite-element model can predict forming defects and be consistent with the actual condition and that the optimization procedure proposed in the paper is feasible. (C) 2013 Politechnika Wroclawska. Published by Elsevier Urban & Partner Sp. z o.o. All rights reserved.

关键词： Aluminum alloy hot stamping Process parameters Response surface model multi-objective genetic algorithm

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Optimal answer generation by equivalent transformation incorporating multi-objective genetic algorithm

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SOFT COMPUTING 2022年第19期26卷 10535-10546页

作者： Miura, Katsunori Powell, Courtney Munetomo, Masaharu Otaru Univ Dept Informat & Management Sci 3-5-21 Midori Otaru Hokkaido 0478501 Japan Hokkaido Univ Informat Initiat Ctr Kita Ku Kita 11Nishi 5 Sapporo Hokkaido 0600811 Japan

This paper proposes a framework for time-efficiently finding multiple answers that meet multiple logical constraints and are Pareto-optimal for multiple objective functions. The proposed framework determines a set of optimal answers by iteratively replacing a set of definite clauses. Clause replacement is performed using a SAT solver consisting of equivalent transformation rules (ETRs). An ETR replaces a definite clause with one or more clauses while preserving the declarative meaning of the union of the original clause and problem clauses. To efficiently find optimal answers, in this paper, we define a new class of ETRs that are generated based on the evaluation results of a multi-objective genetic algorithm (MOGA) and propose a method for generating ETRs that belong to the new class. ETRs belonging to the new class help to replace definite clauses according to user objectives such as cost-benefit performance, reliability, and financial constraints. Thus, a SAT solver that uses the new ETR class in addition to extant ETR classes can preferentially replace definite clauses that produce the optimal answer for user objectives. Experimental results indicate that the proposed framework can significantly reduce the computation time and memory usage necessary to determine a set of optimal answers for user objectives.

关键词： Equivalent transformation multi-objective genetic algorithm Clause replacement Optimization Pareto-optimal answer Predicate logic

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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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