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The distributed CERES-Maize model with crop parameters determined through data assimilation assists in regional irrigation schedule optimization

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2022年 202卷

作者： Wang, Yongqiang Huang, Donghua Zhao, Long Shen, Hongzheng Xing, Xuguang Ma, Xiaoyi Northwest A&F Univ Key Lab Agr Soil & Water Engn Arid & Semiarid Area Minist Educ Yangling 712100 Peoples R China Henan Univ Sci & Technol Coll Agr Equipment Engn Luoyang 471000 Henan Peoples R China

Crop model-based irrigation scheduling is essential for improving crop production and water resource allocation. However, the parametrization of crop growth processes, for which relevant genetic coefficients must be determined through field scale experiments, is usually hampered by poor spatial representation;this limitation compromises the robustness of irrigation scheduling at the regional scale. We propose a framework for optimizing regional irrigation schedules;this framework involves using a distributed crop model with crop parameters determined through data assimilation. We first conducted a sensitivity analysis to identify the key genetic coefficients (P2: photoperiod sensitivity coefficient;P5: thermal time from silking to physiological maturity;G3: kernel filling rate during the linear grain filling stage and under optimal conditions) affecting the leaf area index (LAI) in the CERES-Maize model. Subsequently, the field-scale CERES-Maize model was calibrated and validated using data collected from field experiments conducted in 2016 and 2017, respectively. Subsequently, the LAI estimated from 2020 microwave backscattering data was used to assimilate remote sensing information and crop model in order to obtain the key genetic coefficients of maize in the distributed crop model. Finally, on the basis of the distributed crop model, a multiobjective genetic algorithm was executed to optimize irrigation schedules under various meteorological scenarios (e.g., precipitation and reference evapotranspiration). The LAI of maize at a regional scale was accurately estimated using backscattering information extracted from microwave remote sensing images (R-2 = 0.94, RMSE = 0.27). Compared with the crop model parameters calibrated at the field scale, the crop model parameters determined through data assimilation resulted in more accurate simulations of LAI, soil water content, and yield at the regional scale. The irrigation schedule based on the distributed model increase

关键词： Irrigation schedule Data assimilation Distributed crop model Remote sensing Copula function nondominated sorting genetic algorithm version II

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Superpixel-based multi-point intensity customizable light focusing for reflective iterative optimization wavefront shaping

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OPTIK 2021年 242卷

作者： Zhao, Yang Tang, Zhaoyang Xiang, Chengchao Ding, Yingchun Beijing Univ Chem Technol Coll Math & Phys Beijing 100029 Peoples R China

We propose a new algorithm, which is the combination of the superpixel method and nondominated sorting genetic algorithm, for multi-point light focusing of reflective iterative optimization wavefront shaping. Our algorithm shows a strong ability to control light, which is embodied in the following aspects: (1) Compared with other algorithms, the enhancement factor of multiple focal points is very high;(2) The uniformity of the intensity of multiple focal points is controllable;(3) Especially important is that the intensity of each of the multiple focal points is customizable. In the experiment, when 10 points are focused, the enhancement factor is up to 222.1, and the relative standard deviation is 5.99%. In terms of customizable intensity focusing, we set the ratio of focus intensity of two parts as 1:2 of 10-point, and the experimental result is 1:2.02, showing its powerful optical control capability in customizable intensity focusing. Our method has potential application value in the field of optogenetics and light capture.

关键词： Superpixel method nondominated sorting genetic algorithm Wavefront shaping Multi-point controllable Customizable intensity

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Optimization Parameter Variation: Improving Biobjective Optimization of Temporary Facility Planning

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JOURNAL OF COMPUTING IN CIVIL ENGINEERING 2018年第5期32卷

作者： Khalafallah, Ahmed Hyari, Khaled Hesham Kuwait Univ Dept Civil Engn POB 5969 Safat 13060 Kuwait Cairo Univ Dept Civil Engn Cairo 12613 Egypt Hashemite Univ Dept Civil Engn POB 330127 Zarqa 13133 Jordan

Recent research interest in nondominated sorting genetic optimization has increased remarkably due to its effectiveness in handling multiple objectives without scalarization. algorithms for these evolutionary optimization methods have been used to solve a wide variety of civil engineering optimization problems. Despite the effectiveness of these algorithms, their optimization parameter tuning is a time-consuming process that, if improperly executed, can hinder performance and lead to premature convergence toward incomplete, suboptimal Pareto fronts. Existing tuning methods either require a substantial amount of time or rely on inadequate methods. The main objective of this study is to investigate the performance of these algorithms under the effect of optimization parameter variation. Using a biobjective benchmark problem on optimizing temporary facility planning, the study specifically investigates the effects of varying the population size, number of generations, crossover type, probability of crossover, and mutation rate on the algorithm behavior. The results are used to recommend strategies for parameter tuning in order to accelerate convergence toward optimal Pareto fronts and improve solution dispersion ranges for biobjective optimization problems. The findings of this study should prove most useful to scholars and construction planners, especially those who are involved in temporary facility planning.

关键词： algorithms Construction industry Evolutionary computation Optimization Multiobjective genetic algorithms Multiple objective analysis nondominated sorting genetic algorithm Temporary facility planning

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Pareto Optimal Synthesis of the Linear Array Geometry for Minimum Side lobe Level and Null Control During Beam Scanning

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INTERNATIONAL JOURNAL OF RF AND MICROWAVE COMPUTER-AIDED ENGINEERING 2010年第5期20卷 557-566页

作者： Gunes, F. Tokan, F. Yildiz Tech Univ Dept Elect & Commun Engn Fac Elect & Elect TR-34349 Istanbul Turkey

In this work, synthesis of the linear array geometry is put forward as a constrained vector optimization problem whose components are to meet the minimum sidelobe level (SU) and control of the wide/narrow null placement during beam scanning whose range can vary from zero to the wide bands. As these synthesis objectives generally conflict with each other, nondominated solutions are acquired using the nondominated sorting genetic algorithm- II (NSGA-II) as a fast nondominated genetic sorting algorithm. Then, the Pareto frontiers are obtained using these trade-off solution sets between the maximum SLI,, null control, and scanning range to provide a view of all design options. Thus, the pattern features resulted from these Pardo frontiers are valid for any chosen main beam direction within its full prescribed beam scanning range. The same Pareto optimal synthesis procedure can be applied to a thinned linear antenna array. A thinned linear antenna array is obtained by a simple genetic optimization by rounding the excitation amplitudes either to I or 0 to minimize the maximum of side lobe level (MSLL) during the beam scanning within the prescribed region as stated in the multiobjective function. Finally, some typical Pardo optimal radiation patterns of the scanning arrays are synthesized with only perturbating the positions of the array elements, and their full electromagnetic wave simulations are also completed to examine the resulted mutual coupling effects between the elements of the arrays. It can be concluded that the Pareto optimal synthesis procedure gives the scanning linear antenna arrays with successful radiation performance. (C) 2010 Wiley Periodicals, Inc Int J RF and Microwave CAE 20 557-566.2010

关键词： linear array sidelobe suppression. null control beam scanmilit constrained multiobjective optimization pareto-optimal solutions nondominated sorting genetic algorithm

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Multi-objective optimization of a diesel particulate filter: an acoustic approach

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PARTICULATE SCIENCE AND TECHNOLOGY 2022年第4期40卷 465-474页

作者： Ozturk, Sinem Erol, Haluk Istanbul Tech Univ Mech Engn Fac TR-34437 Istanbul Turkey

In order to decrease the soot particles' harmful emission, it is necessary to use a diesel particulate filter (DPF) on cars. Recent years, many countries standardize using DPF on cars. In this paper, a multi-objective design optimization study is performed by using the nondominated sorting genetic algorithm (NSGA-II) to obtain an optimum DPF geometry. In the present study, two objective functions were determined to obtain an optimum DPF. The first was to maximize sound transmission loss (TL) values to increase the acoustical properties of the DPF, while the second was to minimize the back pressure to cause the diesel engine to work efficiently and consume less energy. The optimization problem has been constructed in the form of maximizing the sum of the values of TL at all frequencies, as optimizing the problem for each frequency separately means a new DPF at each frequency, which has a different geometry. In the literature, optimization of the DPF has been carried out for a few parameters by just changing one parameter value and keeping others stable. However, in this paper, optimization of the acoustic performance of DPFs was performed for more than one parameter in the range of their specific values simultaneously.

关键词： Diesel particulate filter multi-objective optimization nondominated sorting genetic algorithm sound transmission loss pressure drop

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Green synthesis of bioemulsifier and exopolysaccharides by Brevibacillus borstelensis and process parameters optimization using response surface model, genetic algorithm and NSGA

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2021年 291卷 112667页

作者： Dhagat, Swasti Jujjavarapu, Satya Eswari Natl Inst Technol Raipur Dept Biotechnol Raipur Madhya Pradesh India

Bioemulsifier and exopolysaccharides are industrially important biomolecules produced by microorganisms using green technology. They have applications in food, biomedical, pharmaceutical and cosmetic industries and hence high yield of both products becomes necessary. The current study showed that Brevibacillus borstelensis has a potential to produce bioemulsifier and exopolysaccharide simultaneously but yield of both products is limited. In this study, CCD-RSM has been used as experimental design to increase concentration of both products. Concentrations of glucose, monosodium glutamate, yeast extract and magnesium sulphate were process variables and concentrations of bioemulsifiers, exopolysaccharides and biomass were responses. 30 experimental runs were performed and the models from CCD were optimized by genetic algorithm and NSGA. The results from modelling and optimization techniques were compared along with validation of models. The predicted values from optimization techniques were better than experimental values. The study concluded that NSGA is most suitable to optimize multiple responses simultaneously when compared to RSM and genetic algorithm. The optimum conditions for production were 22 g/l glucose, 14 g/l monosodium glutamate, 6 g/l yeast extract and 0.6 g/l magnesium sulphate with maximum yield of 6.1, 17.6 and 2.8 g/l bioemulsifier, exopolysaccharide and biomass, respectively. Knowledge of optimum concentrations of carbon and nitrogen source will help to utilize industrial and agricultural wastes for production of both products. They have applications in environmental bioremediation by clearing oil spills. Bioemulsifiers also help in heavy metal removal from hazardous waste. Hence this will result in environmental bioremediation by utilization of wastes by employing products generated from wastes.

关键词： Bioemulsifier Exopolysaccharides CCD-RSM genetic algorithm Multiobjective optimization nondominated sorting genetic algorithm

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genetic-algorithm-based optimization of an industrial age-hardening operation for packed bundles of aluminum rods

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MATERIALS AND MANUFACTURING PROCESSES 2007年第5-6期22卷 615-622页

作者： Sahay, Satyam S. Mehta, Rajesh Krishnan, Karthik Div Tata Consultancy Serv Ltd Tata Res Dev & Design Ctr Pune 411013 Maharashtra India

Industrial thermal processing operations are highly energy intensive, and must be efficiently operated, especially under the current global energy crisis. However. optimization of such complex operations is a nontrivial task and must be formally approached. In the present work, an integrated model has been used in conjunction with a genetic algorithm to optimize industrial age-hardening of packed bundles of aluminum alloy rods. The presence of peak yield strength and the strong dependence of peak time on age-hardening temperature make this operation interesting for optimization studies. Multiobjective optimization problems have been formulated and solved for energy minimization, productivity maximization, and variability reduction during age-hardening by an elitist nondominated sorting genetic algorithm. The Pareto-front for optimum furnace temperature. time, and quality variation has been determined for isothermal as well as multisegment nonisothermal furnace temperature profiles. Subsequently, optimum multisegment furnace temperature-time set-points were obtained and compared with equivalent isothermal profile. The advantage of multisegment nonisothermal profile over isothermal profile in reducing the energy consumption has been demonstrated.

关键词： age-hardening aluminum alloy energy consumption genetic algorithm integrated model modeling multiobjective nondominated sorting genetic algorithm optimization Pareto-optimality precipitation model productivity product quality thermal model

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Pareto-optimal synthesis of microwave amplifier to design the noise-constrained gain value

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MICROWAVE AND OPTICAL TECHNOLOGY LETTERS 2012年第4期54卷 1079-1084页

作者： Cengiz, Yavuz Konar, Eray Suleyman Demirel Univ Dept Elect & Commun Engn TR-32200 Isparta Turkey

In this work, synthesis of microwave amplifier is put forward as a constrained vector optimization problem whose components are to meet the specific gain value constrained by the minimum noise over the available bandwidth. nondominated solutions are acquired using the nondominated sorting genetic algorithm-II as a fast nondominated genetic sorting algorithm wherefore these synthesis objectives generally conflict with each other. Then, the Pareto frontiers are obtained using these trade-off solution sets between the performance characterization of the device results in the triplet of gain GT, noise F, and input VSWR Vi and its source (ZS) and load (ZL). It can be concluded that the Pareto-optimal synthesis procedure provides microwave amplifier with optimum performance. (c) 2012 Wiley Periodicals, Inc. Microwave Opt Technol Lett 54:10791084, 2012;View this article online at ***. DOI 10.1002/mop.26724

关键词： microwave amplifier constrained multiobjective optimization Pareto-optimal solutions nondominated sorting genetic algorithm

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Application of the SVR-NSGAII to Hydrograph Routing in Open Channels

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JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERING 2016年第3期142卷

作者： Aboutalebi, Mahyar Bozorg Haddad, Omid Loaiciga, Hugo A. Univ Tehran Coll Agr & Nat Resources Fac Agr Engn & Technol Dept Irrigat & Reclamat Engn Tehran *** Iran Univ Tehran Coll Agr & Nat Resources Fac Agr Engn & Technol Dept Irrigat & Reclamat Engn Tehran *** Iran Univ Calif Santa Barbara Dept Geog Santa Barbara CA 93106 USA

Flow routing is used to simulate or predict downstream hydrographs on the basis of the features of upstream flow hydrographs. This paper combines support vector regression (SVR) and the nondominated sorting genetic algorithm II (NSGAII) into a hybrid hydrologic routing model called SVR-NSGAII in this paper for the prediction of a downstream flow hydrograph in simple and compound channels. The SVR-NSGAII hydrologic routing predictions are compared with those from hydraulic models in simple and compound channels. This paper's results indicate that the SVR-NSGAII predicts the downstream hydrograph flow in a simple and compound channel, with approximately 94 and 98% accuracy, respectively.

关键词： Flow routing Open channels Support vector machine nondominated sorting genetic algorithm

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Optimal design of multiproduct batch chemical plant using NSGA-II

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ASIA-PACIFIC JOURNAL OF CHEMICAL ENGINEERING 2006年第1-2期1卷 13-20页

作者： Sarkar, Debasis Modak, Jayant M. Indian Inst Sci Dept Chem Engn Bangalore 560012 Karnataka India

The optimal design of a multiproduct batch chemical plant is formulated as a multiobjective optimization problem, and the resulting constrained mixed-integer nonlinear program (MINLP) is solved by the nondominated sorting genetic algorithm approach (NSGA-II). By putting bounds on the objective function values, the constrained MINLP problem can be solved efficiently by NSGA-II to generate a set of feasible nondominated solutions in the range desired by the decision-maker in a single run of the algorithm. The evolution of the entire set of nondominated solutions helps the decision-maker to make a better choice of the appropriate design from among several alternatives. The large set of solutions also provides a rich source of excellent initial guesses for solution of the same problem by alternative approaches to achieve any specific target for the objective functions. (C) 2006 Curtin University of Technology and John Wiley & Sons, Ltd.

关键词： mixed integer nonlinear programming nondominated sorting genetic algorithm multiobjective optimization batch plant design

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