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Inverse design methods for indoor ventilation systems using CFD-based multi-objective genetic algorithm

BUILDING SIMULATION 2014年第6期7卷 661-669页

作者： Zhai, Zhiqiang (John) Xue, Yu Chen, Qingyan Tianjin Univ Sch Environm Sci & Engn Tianjin 300072 Peoples R China Univ Colorado Boulder CO 80309 USA Purdue Univ Sch Mech Engn W Lafayette IN 47905 USA

Conventional designers typically count on thermal equilibrium and require ventilation rates of a space to design ventilation systems for the space. This design, however, may not provide a conformable and healthy micro-environment for each occupant due to the non-uniformity in airflow, temperature and ventilation effectiveness as well as potential conflicts in thermal comfort, indoor air quality (IAQ) and energy consumption. This study proposes two new design methods: the constraint method and the optimization method, by using advanced simulation techniques-computational fluid dynamics (CFD) based multi-objective genetic algorithm (MOGA). Using predicted mean vote (PMV), percentage dissatisfied of draft (PD) and age of air around occupants as the design goals, the simulations predict the performance curves for the three indices that can thus determine the optimal solutions. A simple 2D office and a 3D aircraft cabin were evaluated, as demonstrations, which reveal both methods have superior performance in system design. The optimization method provides more accurate results while the constraint method needs less computation efforts.

关键词： inverse modeling multi-objective genetic algorithm computational fluid dynamics predicted mean vote percent dissatisfied age of air

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Selection of security system design via games of imperfect information and multi-objective genetic algorithm

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2013年 112卷 59-66页

作者： Lins, Isis Didier Rego, Leandro Chaves Moura, Marcio das Chagas Droguett, Enrique Lopez Univ Fed Pernambuco UFPE CTG CEERMA BR-50740550 Recife PE Brazil Univ Fed Pernambuco UFPE DEP CTG BR-50740530 Recife PE Brazil Univ Fed Pernambuco UFPE DE CCEN BR-50740540 Recife PE Brazil

This work analyzes the strategic interaction between a defender and an intelligent attacker by means of a game and reliability framework involving a multi-objective approach and imperfect information so as to support decision-makers in choosing efficiently designed security systems. A multi-objective genetic algorithm is used to determine the optimal security system's configurations representing the tradeoff between the probability of a successful defense and the acquisition and operational costs. Games with imperfect information are considered, in which the attacker has limited knowledge about the actual security system. The types of security alternatives are readily observable, but the number of redundancies actually implemented in each security subsystem is not known. The proposed methodology is applied to an illustrative example considering power transmission lines in the Northeast of Brazil, which are often targets for attackers who aims at selling the aluminum conductors. The empirical results show that the framework succeeds in handling this sort of strategic interaction. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Selection of compromise solutions Security system design Game of imperfect information multi-objective genetic algorithm

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Minimization of exceptional elements and voids in the cell formation problem using a multi-objective genetic algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第8期38卷 9597-9602页

作者： Arkat, Jamal Hosseini, Leila Farahani, Mehdi Hosseinabadi Univ Kurdistan Dept Ind Engn Sanandaj Iran

Cell formation problem is the main issue in designing cellular manufacturing systems. The most important objective in the cell formation problem is to minimize the number of exceptional elements which helps to reduce the number of intercellular movements. Another important but rarely used objective function is to minimize the number of voids inside of the machine cells. This objective function is considered in order to increase the utilization of the machines. We present a bi-objective mathematical model to simultaneously minimize the number of exceptional elements and the number of voids in the part machine incidence matrix. An epsilon-constraint method is then applied to solve the model and to generate the efficient solutions. Because of the NP-hardness of the model, the optimal algorithms can not be used in large-scale problems and therefore, we have also developed a bi-objective genetic algorithm. Some numerical examples are considered to illustrate the performance of the model and the effectiveness of the solution algorithms. The results demonstrate that in comparison with the epsilon-constraint method, the proposed genetic algorithm can obtain efficient solution in a reasonable run time. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Cell formation problem Exceptional elements Voids epsilon-constraint method multi-objective genetic algorithm

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An optimal energy-efficient clustering method in wireless sensor networks using multi-objective genetic algorithm

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2013年第1期26卷 114-126页

作者： Peiravi, Ali Mashhadi, Habib Rajabi Javadi, S. Hamed Ferdowsi Univ Mashhad Dept Elect Engn Mashhad Iran

In this study, an optimal method of clustering homogeneous wireless sensor networks using a multi-objective two-nested genetic algorithm is presented. The top level algorithm is a multi-objective genetic algorithm (GA) whose goal is to obtain clustering schemes in which the network lifetime is optimized for different delay values. The low level GA is used in each cluster in order to get the most efficient topology for data transmission from sensor nodes to the cluster head. The presented clustering method is not restrictive, whereas existing intelligent clustering methods impose certain conditions such as performing two-tiered clustering. A random deployed model is used to demonstrate the efficiency of the proposed algorithm. In addition, a comparison is made between the presented algorithm other GA-based clustering methods and the Low Energy Adaptive Clustering Hierarchy protocol. The results obtained indicate that using the proposed method, the network's lifetime would be extended much more than it would be when using the other methods. Copyright (C) 2011 John Wiley & Sons, Ltd.

关键词： clustering energy-efficient algorithm multi-objective genetic algorithm wireless sensor network

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

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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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Dynamic configuration of QC allocating problem based on multi-objective genetic algorithm

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JOURNAL OF INTELLIGENT MANUFACTURING 2017年第3期28卷 847-855页

作者： Liang, ChengJi Li, MiaoMiao Lu, Bo Gu, Tianyi Jo, Jungbok Ding, Yi Shanghai Maritime Univ Logist Res Ctr Shanghai Peoples R China Dalian Univ Inst E Commerce & Modern Logist Dalian Peoples R China UCAS Sch Management Beijing Peoples R China Shanghai Int Port Grp Co Ltd Shanghai Peoples R China Dongseo Univ Div Comp & Informat Engn Busan South Korea

Solving the problem of allocating and scheduling quay cranes (QCs) is very important to ensure favorable port service. This work proposes a bi-criteria mixed integer programming model of the continual and dynamic arrival of several vessels at a port. A multi-objective genetic algorithm is applied to solve the problem in three cases. The results thus obtained confirm the feasibility and effectiveness of the model and GA. Additionally, the multi-objective solution considering both the total duration for which vessels stay in the port and QCs move is the best, as determined by comparing with considering only the total time for which vessels stay in the port or QCs move, as it considers, and it balances these two objectives.

关键词： Port facilities multi-objective genetic algorithm Quay crane scheduling Pareto optimal solution

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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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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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