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Assessment of Effective Monitoring Sites in a Reservoir Watershed by Support Vector Machine Coupled with multi-objective genetic algorithm for Sediment Flux Prediction during Typhoons

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WATER RESOURCES MANAGEMENT 2021年第8期35卷 2387-2408页

作者： Jhong, Bing-Chen Fang, Hsi-Ting Huang, Cheng-Chia Natl Taiwan Univ Sci & Technol Dept Civil & Construct Engn Taipei 106335 Taiwan Taiwan Integrated Disaster Prevent Technol Engn C New Taipei 236654 Taiwan Natl Taipei Univ Business Ctr Gen Educ Taipei 100026 Taiwan

Effectively assessing crucial monitoring sites with suspended sediment concentration (SSC) is a vital challenge for achieving accurate prediction of sediment flux on sluice gates at a dam in a reservoir watershed. To address this issue, an assessment framework based on a core concept of Data-Information-Knowledge-Wisdom (DIKW) hierarchy is proposed in this study. First, for the reasonable training of the coupled method, a two-dimentional layer-averaged density current model, SRH2D, is applied to simulate reasonable SSC data. The limited SSC data at monitoring sites collected from the field and at dam face, inflow, and outflow discharges are collected for validation of a calibrated numerical model. Second, a well-known data-driven method, Support Vector Machine (SVM), is coupled with multi-objective genetic algorithm (MOGA) as a sediment-flux-prediction (SFP) model in the proposed framework to evaluate effective monitoring sites with SSC. An application in the Shih-Men Reservoir is implemented to demonstrate the contribution of the proposed investigation framework. The results indicate that the spatial turbidity current movement is reasonably simulated by the numerical model and appropriate as reliable data for the SFP model. The SSCs at measured points located on the lower level at dam face are significantly higher. Moreover, the results also show that the simulated SSC at the monitoring sites located near the inflow point and dam face are relatively useful for SFP. The analyzed results are concluded that the well-established observation equipment at the inflow point and near the dam is necessary for obtaining high-quality measured data, which has become a significant key issue on reservoir operation management (ROM). Also, the proposed framework is expected to be helpful to improve the benefit of ROM as reference for decision makers.

关键词： Sediment flux prediction Suspended sediment concentration Reservoir Support vector machine multi-objective genetic algorithm Typhoon

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Zonal control for selective catalytic reduction system using a model-based multi-objective genetic algorithm

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INTERNATIONAL JOURNAL OF ENGINE RESEARCH 2021年第3期22卷 911-920页

作者： Wang, Guoyang Qi, Jinzhu Liu, Shiyu Li, Yanfei Shuai, Shijin Wang, Zhiming Shandong Univ Sch Energy & Power Engn Jinan Shandong Peoples R China Tsinghua Univ State Key Lab Automot Safety & Energy Beijing 100084 Peoples R China

It is challenging for aqueous urea injection control to achieve high NOconversion efficiency while restricting tailpipe ammonia (NH3) slip. Optimizing the selective catalytic reduction systems can reduce diesel engine emissions, potentially improve fuel economy and urea utilization efficiency, and finally reduce aftertreatment costs. In this article, a model-based multi-objective genetic algorithm is adopted to optimize selective catalytic reduction systems related to trade-off between NOemission and NH3 slip. Selective catalytic reduction model is a one-state selective catalytic reduction model based on continuous stirred tank reactor theory, which significantly reduces the computational burden. The optimal NH3 coverage ratio map was obtained globally based on world harmonized transient cycle. The effect of temperature on optimal NH3 coverage ratio, Zonal control logics extracted from the optimal solution, and the control problems on different zones were analyzed. The zonal control logics were validated on multiple test cycle with different initial NH3 coverage ratios. Results show that the zonal control achieves high NOconversion while restricting the tailpipe NH3 slip. With this method, NOemission and NH3 slip of optimal solution can meet the requirements of the Euro VI emission regulation for heavy-duty diesel engines.

关键词： Heavy-duty diesel engine NO emission control selective catalytic reduction ammonia coverage ratio optimization multi-objective genetic algorithm

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An Efficient Real-Time Vehicle Classification from a Complex Image Dataset Using eXtreme Gradient Boosting and the multi-objective genetic algorithm

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PROCESSES 2024年第6期12卷 1251页

作者： Mani, Pemila Komarasamy, Pongiannan Rakkiya Goundar Rajamanickam, Narayanamoorthi Alroobaea, Roobaea Alsafyani, Majed Afandi, Abdulkareem SRM Inst Sci & Technol Dept Elect & Elect Engn Chennai 603203 India SRM Inst Sci & Technol Dept Comp Technol Chennai 603203 India Taif Univ Coll Comp & Informat Technol Dept Comp Sci POB 11099 Taif 21944 Saudi Arabia Univ Business & Technol Gen Subject Dept Jeddah 23435 Saudi Arabia

Recent advancements in image processing and machine-learning technologies have significantly improved vehicle monitoring and identification in road transportation systems. Vehicle classification (VC) is essential for effective monitoring and identification within large datasets. Detecting and classifying vehicles from surveillance videos into various categories is a complex challenge in current information acquisition and self-processing technology. In this paper, we implement a dual-phase procedure for vehicle selection by merging eXtreme Gradient Boosting (XGBoost) and the multi-objective Optimization genetic algorithm (Mob-GA) for VC in vehicle image datasets. In the initial phase, vehicle images are aligned using XGBoost to effectively eliminate insignificant images. In the final phase, the hybrid form of XGBoost and Mob-GA provides optimal vehicle classification with a pioneering attribute-selection technique applied by a prominent classifier on 10 publicly accessible vehicle datasets. Extensive experiments on publicly available large vehicle datasets have been conducted to demonstrate and compare the proposed approach. The experimental analysis was carried out using a myRIO FPGA board and HUSKY Lens for real-time measurements, achieving a faster execution time of 0.16 ns. The investigation results show that this hybrid algorithm offers improved evaluation measures compared to using XGBoost and Mob-GA individually for vehicle classification.

关键词： vehicle classification machine learning eXtreme gradient boost algorithm multi-objective genetic algorithm

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Credit portfolio optimization: A multi-objective genetic algorithm approach

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BORSA ISTANBUL REVIEW 2022年第1期22卷 69-76页

作者： Wang, Zhi Zhang, Xuan Zhang, ZheKai Sheng, Dachen Beijing Univ Technol Int Business Sch Gengdan Inst Beijing Peoples R China Nanjing Audit Univ Inst Econ & Finance Nanjing Peoples R China

The algorithm for optimization of a credit portfolio has not been fully demonstrated. This paper fills the gap in the literature by presenting a general approach for optimizing a credit portfolio by minimizing the default risk of the entire portfolio. Default risk is measured with quadratic weighting and a matrix containing information about the default intensity of two stocks and the correlation in default between them. The default correlation and the default intensity are represented with a novel bivariate intensity model. A multi-objective genetic algorithm is introduced to optimize a credit portfolio with the purpose of overcoming limitations in the analytical method and improving the efficiency of optimization. The algorithm can be applied to a portfolio's credit risk management, which is particularly crucial for investors and regulars in emerging markets. Copyright (C) 2021, Borsa Istanbul Anonim Sirketi. Production and hosting by Elsevier B.V.

关键词： Credit portfolio optimization Heuristic optimization multi-objective genetic algorithm

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Enhancement of wind energy resources assessment using multi-objective genetic algorithm: A case study at Gabal Al-Zayt wind farm in Egypt

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INTERNATIONAL JOURNAL OF GREEN ENERGY 2021年第14期18卷 1497-1509页

作者： Shaltout, Mohamed L. Mostafa, Moaz A. Metwalli, Sayed M. Cairo Univ Fac Engn Mech Design & Prod Engn Dept Giza 12613 Egypt

Estimation of wind speed distribution is essential for wind energy resources assessment, design of wind farms, and selection of suitable wind turbines. Two-parameter Weibull distribution function is widely used worldwide for wind energy resources assessment. As a case study, 1one-year field measurements at Gabal Al-Zayt wind farm in Egypt are used to estimate the Weibull parameters and to accurately assess the wind energy resource. In this work, seven statistical methods are adopted to estimate the Weibull parameters and their estimation accuracy is compared based on some common estimation errors. However, the improvement in one estimation error does not necessarily improve other types of errors. Consequently, a multi-objective genetic algorithm (MOGA) is adopted to investigate the tradeoffs among the competing estimation errors and to enhance the assessment of wind energy resources. The results show significant improvement in the estimation accuracy of the Weibull parameters using MOGA as compared to conventional statistical estimation methods. On the other hand, the case study at Gabal Al-Zayt wind farm reveals that the selection of wind turbines does not depend only on wind characteristics of the site but also on its environmental characteristics.

关键词： Wind energy assessment Gabal Al-Zayt multi-objective genetic algorithm Weibull distribution Statistical estimation

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Distributed Data Real-Time Transaction Calculation Based on Collaborative Optimization and multi-objective genetic algorithm

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INTERNATIONAL JOURNAL OF INTELLIGENT INFORMATION TECHNOLOGIES 2024年第1期20卷 1页

作者： Liao, Li Chongqing City Vocat Coll Chongqing Peoples R China

Based on the above situation, this article elaborated on the methods that should be used to calculate the multi-information genetic algorithm (GA) in the current situation. The article mainly compared the non-dominated sorting genetic algorithm-II (NSGA-II) and multi-objective particle swarm optimization (MOPSO) with elite strategy. By measuring the solving speed and quality of the two algorithms, it was found that the NSGA-II had a greater advantage. Based on the NSGA-II, optimization processing was carried out. The NSGA-II was compared before and after optimization. After analyzing 48 data samples, it was found that the results of the NSGA-II before and after optimization showed that the algorithm tended to be more stable after optimization, thus indicating that the improved data was more accurate. The results indicated that the NSGA-II was necessary for its improvement, and its results were also reasonable.

关键词： Distributed Data MOPSO algorithm multi-objective genetic algorithm NSGA-II algorithm Real-Time Transaction Computing

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multi-objective genetic algorithm optimization of artificial neural network for estimating suspended sediment yield in Mahanadi River basin, India

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INTERNATIONAL JOURNAL OF RIVER BASIN MANAGEMENT 2020年第2期18卷 207-215页

作者： Yadav, Arvind Satyannarayana, Penke Koneru Lakshmaiah Educ Fdn Dept Elect & Comp Engn Vaddeswaram 522502 AP India Natl Inst Technol Dept Min Engn Rourkela India

Rivers are an integral part of the hydrological cycle and are the major geological agents which erode the continents and transport water and sediments to the oceans. Thus rivers act an important link between continents and oceans for the mass balance. Estimating the suspended sediment yield is one of the crucial aims in the field of managing, designing and planning of any river system or reservoir. To determine the suspended sediment yield in a river basin demands more labour or it is more costly when automatic sampling devices are used. The number of variables and the inter-relationship existing among them influence the suspended sediment yield and the nature of these inter-relationships is neither linear nor simple. Unfortunately, it is a difficult task to determine the suspended sediment yield when traditional mathematical models are used as complex variables and processes are involved. The major key factors, such as basin geology (rock type), relief, rainfall, temperature, water discharge and catchment area that affect sediment yield, are used as inputs to develop the model for predicting the suspended sediment yield in the Mahanadi River. In this paper, a multi-objective genetic algorithm for artificial neural network (MOGA-ANN)-based approach is used for predicting the suspended sediment yield. The MOGA assists ANN to minimize the two competing objectives, i.e. mean error and variance simultaneously. Thus in this study, a hybrid artificial intelligence-based method, MOGA-ANN model, is developed using the hydro-geological-climatic factors where all parameters associated with the ANN models are optimized simultaneously using MOGAs to estimate the suspended sediment yield in the Mahanadi River basin. The ANN's parameters are optimized globally by the MOGA to accurate estimation. The study has been carried out to develop MOGA-ANN for estimating the suspended sediment load using 20-year data at the Tikarapara gauging station which is the last downstream station in

关键词： Artificial neural network multi-objective genetic algorithm water discharge suspended sediment yield Mahanadi River

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Design optimization of the parallel-feeding multi-effect evaporation system using multi-objective genetic algorithm

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DESALINATION AND WATER TREATMENT 2021年 224卷 33-46页

作者： Liu, Cong Bi, Mingshu Cui, Guangrui Dalian Univ Technol Sch Chem Machinery Dalian 116023 Peoples R China

multi-effect evaporation (MEE) system design is a complex task and affected by a series of variables. Design optimization of the parallel-feeding multi-effect evaporation system using a multi-objective genetic algorithm is studied in the paper. Gain output ratio (GOR) and simplified cost of water are considered as two objective functions and the number of the effect (n), the top brine temperature (T-b), the apparent temperature difference (Delta f), and the recovery ratio of the first effect (RR1) are defined as the input variables. It is found that for satisfying the objective function requirement the top brine temperature (T-b) and recovery ratio (RR1) are always the upper limits of the simulation interval, which are 80 degrees C and 4, respectively. Simultaneously, two design approaches DS and DTD and two evaluation criteria optimal yield and optimal economical are proposed to evaluate the various optimal solutions. Two case studies are presented to illustrate the optimization process and result selection in detail. The multi-objective genetic algorithm proposed in the paper not only can optimize the existing scheme but also can provide several scenarios with their advantages to decision-makers at the design process. The present study has demonstrated the successful application of a multi-objective genetic algorithm for the optimal design of parallel-feeding configuration.

关键词： Parallel feed multi-effect evaporation Design optimization multi-objective genetic algorithm

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Comparing the performance of four shading strategies based on a multi-objective genetic algorithm: A case study in a university library

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JOURNAL OF BUILDING ENGINEERING 2023年 63卷

作者： Wen, Shikang Hu, Xiao Hua, Guanjun Xue, Peng Lai, Dayi Shanghai Jiao Tong Univ Sch Design Dept Architecture Shanghai 200240 Peoples R China Beijing Univ Technol Beijing Key Lab Green Built Environm & Energy Eff Beijing 100124 Peoples R China

Through the control of excessive daylight in buildings, shading devices can reduce glare and improve occupants??? visual comfort. However, shading devices may overly reduce illuminance levels. Many shading strategies are available, but they do not perform equally well, and tradi-tionally, it has been difficult to select the most suitable shading strategy. This study proposed a parameterization method for the selection and design of a shading strategy that would reduce glare while maintaining a satisfactory daylighting level, through the construction of Pareto sets based on a multi-objective genetic algorithm. The objective function was created by combining a dynamic glare evaluation indicator, the spatial glare autonomy (sGA), and a self-constructed daylight index, the spatial daylight vote autonomy (sDVA), developed from a field survey. As a case study, the proposed method was used to compare the performance of four shading strategies, including vertical slats (Vs), a perforated aluminum sheet (PAS), serrated windows with southern orientation (Sw_S), and serrated windows with northern orientation (Sw_N) in a university library in Shanghai, China. It was found that the Sw_S and PAS had a relatively bad performance;the Vs performed better than the Sw_N in providing a more satisfactory illuminance level;and the Sw_N was more effective in reducing the glare. The multi-objective optimization process can be used to obtain near-optimal design parameters that create a visual environment with reduced glare while maintaining an acceptable illuminance level, for all four shading strategies. The developed method can be a helpful tool in the design of an appropriate daylighting environment.

关键词： multi-objective genetic algorithm Shading strategy optimization Daylight environment Glare Visual comfort Pareto set Artificial neural network

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Optimization of multi-Mission CubeSat Constellations with a multi-objective genetic algorithm

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REMOTE SENSING 2023年第6期15卷 1572-1572页

作者： Melaku, Shimeles Demissie Kim, Hae-Dong Univ Sci & Technol Aerosp Syst Engn Daejeon 34113 South Korea Gyeongsang Natl Univ Dept Aerosp & Software Engn Jinju 52828 South Korea

The increasing demand for low-cost space-borne Earth observation missions has led to small satellite constellation systems development. CubeSat platforms can provide a cost-effective multiple-mission space system using state-of-the-art technology. This paper presents a new approach to CubeSat constellation design for multiple missions using a multi-objective genetic algorithm (MOGA). The CubeSat constellation system is proposed to perform multi-missions that should satisfy global Earth observation and regional disaster monitoring missions. A computational approach using a class of MOGA named non-dominated sorting genetic algorithm II is implemented to optimize the proposed system. Pareto optimal solutions are found that can minimize the number of satellites and the average revisit time (ART) for both regional and global coverage while maximizing the percentage coverage. As a result, the study validates the feasibility of implementing the CubeSat constellation design with an acceptable level of performance in terms of ART and percentage coverage. Moreover, the study demonstrates CubeSat's ability to perform a multi-missions.

关键词： CubeSat multi-mission constellation multi-objective genetic algorithm

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