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9th IEEE International Conference on Industrial Informatics (INDIN)

作者： Bensmaine, Abderrahmane Benyoucef, Lyes Dahane, Mohammed INRIA Nancy Grand Est ISGMP ISGMP Bat A F-57000 Metz France LGIPM ENIM F-57078 Metz France

ISBN: (纸本)9781457704345

Reconfigurable manufacturing system (RMS) is a manufacturing paradigm that cost-effectively responses to market changes. This paper addresses the multi-objective process plan generation problem in RMS. More specifically, two metaheuristics, namely Non-dominated sorting genetic algorithm (NSGA-II) and Archived Multi-Objective Simulated Annealing (AMOSA), are adapted to generate near-optimal process plans. With the help of a numerical example, the performances of the two metaheuristics are demonstrated and compared.

关键词： AMOSA Encoding genetic algorithms Manufacturing systems Process planning RMS Simulated annealing Transportation adapted NSGA-II archived multiobjective simulated annealing genetic algorithms manufacturing paradigm manufacturing systems multiobjective process plan generation problem nondominated sorting genetic algorithm process planning reconfigurable manufacturing systems simulated annealing

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Simultaneous Prediction Interval-Based Multiobjective Solution Approach for Multiple Quality Characteristics Optimization 5

Simultaneous Prediction Interval-Based Multiobjective Soluti...

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5th International Conference on Industrial Engineering and Applications (ICIEA)

作者： Sharma, Abhinav Kumar Mukherjee, Indrajit Indian Inst Technol Shailesh J Mehta Sch Management Bombay Maharashtra India

ISBN: (纸本)9781538657485

Simultaneous optimization of multiple quality characteristics is a critical and difficult task for researchers and practitioners. This is primarily due to presence of correlation between responses and therefore tradeoff between them is inevitable. Thus, there is no single global optimal solution for such problems. Such problems are also known as multiple response optimization (MRO) problems. Among the different solution approaches proposed for MRO problems, Pareto front solution is one such alternative. However, there is no evidence of systematic work that addresses response uncertainty considering simultaneous prediction interval. This paper illustrates a systematic approach to generate Pareto solutions using multiobjective optimization (MOO) techniques for MRO problems considering appropriate simultaneous prediction intervals. The proposed solution approach is verified using two MRO case instances. Pareto solutions for MRO problems are generated using two MOO strategy [Non-dominated sorting genetic algorithm (NSGA-II) and Multiobjective Particle Swarm Optimization (MOPSO)]. A comparative study shows that NSGA-II provides better Pareto fronts than MOPSO. The MRO solution quality of NSGA-II is also found to be encouraging for future research.

关键词： multiobjective optimization multiobjective particle swarm optimization multiple quality characteristics multiple response optimization nondominated sorting genetic algorithm simultaneous prediction interval

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Optimization of a Robotaxi Dispatch Problem in Pandemic Era

Optimization of a Robotaxi Dispatch Problem in Pandemic Era

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Li, Mengqi Qi, Liang Zhang, Rongyan Luan, Wenjing Guo, Xiwang Coll. of Comput. Sci. and Eng. Shandong Univ. of Sci. and Tech. Qingdao China Coll. of Comp. and Comm. Eng. Liaoning Petroch. Univ. Fushun China

ISBN: (纸本)9798350337020

Autonomous driving has been successfully realized in particular areas such as logistics distribution centers, container terminals, and university campuses. Robotaxi could be another potential application in the near future. This work studies a robotaxi dispatch problem during the pandemic time. It proposes a multi-objective optimization model to minimize the number, waiting time, and driving distance of the robotaxis. Besides, a dispatch strategy is innovatively designed according to a defined severity degree of the pandemic. A virus infection rate can be decreased by reducing contact among passengers. A two-stage nondominated sorting genetic algorithm (NSGA-TS) is proposed to solve the problem. Three operations are used to generate offspring solutions, which can ensure the diversity of the population and speed up the convergence of the algorithm. The effectiveness of NSGA-TS is verified compared with two popular multi-objective optimization algorithms, i.e., multi-objective evolutionary algorithm based on decomposition (MOEA/D) and nondominated sorting genetic algorithm II (NSGA-II). Experimental results show that the proposed model performs well on the studied problem. It can reduce the virus infection rate by decreasing contact among passengers at different risk levels of the pandemic while accomplishing passenger orders. This work is conducive to society building intelligent transportation in the post-pandemic era. © 2023 IEEE.

关键词： Autonomous driving infection rate multi-objective optimization nondominated sorting genetic algorithm robotaxi dispatch

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Optimal Monthly Reservoir Operation Rules for Hydropower Generation Derived with SVR-NSGAII

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JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT 2015年第11期141卷 04015029-04015029页

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

A novel tool is proposed that couples the nondominated sorting genetic algorithm (NSGAII) with support vector regression (SVR) and nonlinear programming (NLP) to optimize monthly operation rules for hydropower generation. The SVR-NSGAII is applied to calculate the optimized release for hydropower generation by minimizing (1) the error committed by the SVR in extracting the optimized operation rule, and (2) the number of input variables used as predictors (the parsimony feature) in a regression model. The SVR calculates the optimized reservoir release for hydropower generation based on input variables and parameters values that are found by the NSGAII. An evaluation of results obtained for the Karoon-4 reservoir of Iran indicates that the SVR-NSGAII is well suited to calculate the optimal hydropower reservoir operation rule in real time with approximately 90% accuracy. (C) 2015 American Society of Civil Engineers.

关键词： Support vector regression nondominated sorting genetic algorithm Hydropower Optimal operation rule

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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 167319-167319页

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 04018036-04018036页

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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Multi-Objective Optimization of Convective Heat Transfer for a Composite Internal and Film Cooling Structure

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ASME JOURNAL OF HEAT AND MASS TRANSFER 2023年第3期145卷 031802-031802页

作者： Zhang, Guohua Zhu, Huaitao Xie, Gongnan Sunden, Bengt Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Peoples R China Northwestern Polytech Univ Sch Mech Engn Xian 710072 Peoples R China Northwestern Polytech Univ Shenzhen Res & Dev Inst Shenzhen 518057 Peoples R China Lund Univ Dept Energy Sci SE-22100 Lund Sweden

This paper conducted a multi-objective optimization work for a composite internal and film cooling structure. The pitch-to-height ratio of the ribs, the inclination angle of the ribs, and the inclination angle of the film hole are chosen as the three design variables to enhance the heat transfer performance, improve the film cooling effectiveness and reduce the pressure loss of the internal channel flow. During the optimization process, the Latin hypercube sampling method is adopted to select 26 sample points from the design space. The response values with higher fidelity at the sample points are calculated using computational fluid dynamics (CFD) simulations. Among the 26 sample points, 21 are used to construct a surrogate model of each objective function while the rest of them are adopted to validate the correctness of the established surrogate model. By combining the Kriging surrogate model with a nondominated sorting genetic algorithm, the Pareto optimal front is obtained after the optimization process. Finally, comparison and analysis are conducted with respect to the cooling performance and mechanisms between the reference model and the selected three representative optimized models. Results show that the optimized three models can not only improve the film cooling effectiveness but also reduce the pressure loss of the channel flow and enhance the heat transfer. In addition, it is found that the optimized model induces an anticlockwise rotating vortex, which entrains more coolant near the target surface. The inclined ribs of the optimized models induce a secondary flow along the inclined ribs, which enhances the flow mixing and augments the heat transfer performance.

关键词： multi-objective optimization gas turbine cooling surrogate model nondominated sorting genetic algorithm flow characteristics

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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 112667-112667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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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 015061-1-015061-9页

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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