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Applying multi-objective genetic algorithm (MOGA) to optimize the energy inputs and greenhouse gas emissions (GHG) in wetland rice production

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ENERGY REPORTS 2020年 6卷 2988-2998页

作者： Elsoragaby, Suha Yahya, Azmi Mahadi, Muhammad Razif Nawi, Nazmi Mat Mairghany, Modather Elhassan, Sami Mustafa M. Kheiralla, A. F. Univ Putra Malaysia Fac Engn Dept Biol & Agr Engn Serdang 43400 Selangor De Malaysia Univ Khartoum Fac Engn Dept Agr & Biol Engn Khartoum Sudan Univ Teknol PETRONAS Inst Self Sustainable Bldg Seri Iskandar Perak Malaysia

Efficient use of energy in crops production will minimize greenhouse gas emission (GHG), prevent destruction of natural resources, and promote sustainable agriculture as an economical crop production system. The aim of this study is applying the multi-objective genetic algorithm MOGA to optimize the energy inputs and reduce the greenhouse gas emissions (GHG) for wetland rice production in Malaysia. The developed multi-objective genetic algorithm (MOGA) model, showed an excess of energy inputs used by the farmers more than the required energy by 37.8% and 40% for the transplanting and broadcast seeding methods. The potential of GHG emissions reduction by MOGA was computed as 95.89 and 236.13 kg CO2eq/ha. Nitrogen represents the highest contributor to the reduction of both, total energy input and total GHG emissions in the two cultivation methods transplanting and broadcast seeding methods. Despite lower consumption of inputs by MOGA, crop yield is estimated at 9.4 ton/ha in transplanting and 9.2 ton/ha in broadcast seeding, which is close to the region's maximum under current condition. The main finding that MOGA model showed an excess of energy inputs used and the potential of GHG emissions reduction was 19.6% and 46.37%. for the transplanting and broadcast seeding methods. (C) 2020 The Author(s). Published by Elsevier Ltd.

关键词： Energy analysis Greenhouse gas emissions (GHG) Optimization multi-objective genetic algorithm

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Tourists Initial Optimal Shunt Scheme Using multi-objective genetic algorithm

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WIRELESS PERSONAL COMMUNICATIONS 2018年第4期102卷 3517-3527页

作者： Hu, Tao Jin, Maozhu Lei, Xia Liao, Zhixue Ge, Peng Sichuan Univ Business Sch Chengdu Sichuan Peoples R China Univ Elect Sci & Technol China Natl Key Lab Sci & Technol Commun Chengdu Sichuan Peoples R China Southwestern Univ Finance & Econ Sch Business Adm Chengdu Sichuan Peoples R China

With the rapid development of tourism, it not only brings great economic benefits, but also causes some problems. Overcrowded visitors reduce tourists satisfaction and bring about negative impact on ecological environment. Designing a reasonable tourists initial shunt scheme can help the management of the scenic area to achieve the goal that balances the economic development and environment protection. In this paper, for the sake of alleviating congestion in scenic area, a tourists optimal shunt scheme is proposed. Instead of defining single optimal objective, the proposed scheme taking two objectives into account, which are minimizing the total load balance degree of the scenic spots and one spot's load degree. In order to estimate optimal shunt ratios, a multi-objective optimization based on genetic algorithm is used to find the Pareto solution. Then a simulation model is built to investigate and verify the scheme. Finally, a comparison analysis validates the efficiency of the model in mitigating the load of the scenic spots in Jiuzhai Valley.

关键词： Tourist shunt Optimization scheme multi-objective genetic algorithm Simulation

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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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Stochastic sampling design using a multi-objective genetic algorithm and adaptive neural networks

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ENVIRONMENTAL MODELLING & SOFTWARE 2009年第4期24卷 530-541页

作者： Behzadian, Kourosh Kapelan, Zoran Savic, Dragan Ardeshir, Abdollah Univ Exeter Ctr Water Syst Exeter EX4 4QF Devon England Amir Kabir Univ Technol Environm Res Ctr Tehran Iran Amir Kabir Univ Technol Dept Civil & Environm Engn Tehran Iran

This paper presents a novel multi-objective genetic algorithm (MOGA) based on the NSGA-II algorithm, which uses metamodels to determine optimal sampling locations for installing pressure loggers in a water distribution system (WDS) when parameter uncertainty is considered. The new algorithm combines the multi-objective genetic algorithm with adaptive neural networks (MOGA-ANN) to locate pressure loggers. The purpose of pressure logger installation is to collect data for hydraulic model calibration. Sampling design is formulated as a two-objective optimization problem in this study. The objectives are to maximize the calibrated model accuracy and to minimize the number of sampling devices as a surrogate of sampling design cost. Calibrated model accuracy is defined as the average of normalized traces of model prediction covariance matrices, each of which is constructed from a randomly generated sampling set of calibration parameter values. This method of calculating model accuracy is called the 'full' fitness model. Within the genetic algorithm search process, the full fitness model is progressively replaced with the periodically (re)trained adaptive neural network metamodel where (re)training is done using the data collected by calling the full model. The methodology was first tested on a hypothetical (benchmark) problem to configure the setting requirement. Then the model was applied to a real case study. The results show that significant computational savings can be achieved by using the MOGA-ANN when compared to the approach where MOGA is linked to the full fitness model. When applied to the real case study, optimal solutions identified by MOGA-ANN are obtained 25 times faster than those identified by the full model without significant decrease in the accuracy of the final solution. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Water distribution system Calibration Uncertainty multi-objective genetic algorithm Adaptive neural networks

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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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Springback control of sheet metal forming based on the response-surface method and multi-objective genetic algorithm

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MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING 2009年第1-2期499卷 325-328页

作者： Liu Wei Yang Yuying Xing Zhongwen Zhao Lihong Harbin Inst Technol Sch Mat Sci & Engn Harbin 150001 Peoples R China

An automated optimization method by the integration of the response-surface method and an optimization algorithm is presented to control springback of stamping parts. In order to minimize both objective functions of springback and thickness deformation simultaneously, a multi-objective genetic algorithm is applied to find all the optimal solutions at one run instead of transforming multi-objective functions into a single objective function. The response-surface model is employed as a fast analysis tool to surrogate the time-consuming finite-element procedure in the iterations of the multi-objective genetic algorithm. An example is studied to illustrate the application of the approach proposed, and it is concluded that the proposed method is more efficient than the traditional manual finite-element procedure and the 'trial and error' approach for springback controlling. (C) 2008 Elsevier B.V. All rights reserved.

关键词： Springback Sheet metal Response-surface method multi-objective genetic algorithm

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multi-objective genetic algorithm approach for enhanced cumulative hydrogen and methane-rich syngas emission through co-pyrolysis of de-oiled microalgae and coal blending

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RENEWABLE ENERGY 2024年 225卷

作者： Rawat, Shweta Wagadre, Lokesh Kumar, Sanjay Indian Inst Technol BHU Varanasi Sch Biochem Engn Varanasi 221005 Uttar Pradesh India Bipin Tripathi Kumaon Inst Technol Biochem Engn Dept Dwarahat 263653 Uttaranchal India

Towards establishing low -carbon bio-economy, the energy -rich syngas is considered a global energy carrier. Targeting hydrogen as a promising advanced fuel, the significance of methane also increases due to its direct conversion capability into hydrogen. The current study aims to use a co -pyrolysis -based valorization of de -oiled microalgae and low -rank coal blend to generate H-2 and CH4 rich syngas. Pyrolysis kinetic models, KissingerAkahira-Sunose (KAS) and Starink (STK) are used to evaluate apparent activation energy (Ea). The gradual addition of microalgae (0-100%) in coal reduces E-a from 189.11-55.87 kJ/mol and 180.16-54.61 kJ/mol by KAS and STK method, respectively. The maximum hymethane carrying ratio is observed 2.51 and 3.51 at optimized conditions of response surface methodology (RSM) and artificial neural network -based multi -objective genetic algorithm (ANN-MOGA), respectively. Maximum H-2 (54.5 %) in the syngas is observed at mid pyrolysis stage (451 C-degrees) using ANN-MOGA optimized conditions (blending ratio - 42.25 % and heating rate -13.8 C/min). This study highlights the advantage of ANN-MOGA optimization over statistical based optimization. Hence, incorporating of the evolutionary algorithm as integrated ANN-MOGA optimization could be an efficient way for hymethane rich syngas emission in co -pyrolysis approach to gain carbon -neutral energy with zero waste discharge.

关键词： De-oiled microalgae Co-pyrolysis Syngas Artificial neural network multi-objective genetic algorithm Hymethane carrying ratio

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An improved multi-objective genetic algorithm for fuzzy flexible job-shop scheduling problem

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2013年第2-3期47卷 280-288页

作者： Wang, Xiaojuan Li, Wenfeng Zhang, Ying Wuhan Univ Technol Sch Logist Engn Wuhan Peoples R China

In many real-world applications, processing time may vary dynamically due to human factors or operating faults and there are some other uncertain factors in the scheduling problems. In this paper, fuzzy sets are used to model uncertain processing time and due date. In addition, an improved multi-objective genetic algorithm is presented to solve the multi-objective fuzzy Flexible Job-shop Scheduling Problem (FJSP). About this improved multi-objective genetic algorithm, Pareto-optimality is applied, including the non-dominated sorting scheme and an improved elite reservation strategy based on NSGA-II. Meanwhile, the immune and entropy principle is used to preserve the diversity of individuals. Moreover, the advanced crossover and the mutation operators are used to adapt to the special chromosome structure. The computational results demonstrate the effectiveness of the proposed algorithm.

关键词： fuzzy FJSP multi-objective genetic algorithm pareto-optimality immune and entropy principle

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Turning of Carbon Fiber Reinforced Polymer (CFRP) Composites: Process Modeling and Optimization using Taguchi Analysis and multi-objective genetic algorithm

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MANUFACTURING LETTERS 2022年 33卷 29-40页

作者： Rob, S. M. Abdur Srivastava, Anil K. Univ Texas Rio Grande Valley 1201 W Univ Dr Edinburg TX 78539 USA

Carbon Fiber Reinforced Polymer (CFRP) composites have been widely used in aerospace, automotive, nuclear, and biomedical industries due to their high strength-to-weight ratio, corrosion resistance, durability, and excellent thermo-mechanical properties in non-oxidative atmospheres. Machining of CFRP composites has always been a challenge for manufacturers. In this research, a comparative study was performed between the optimal machining parameters of coated and uncoated carbide inserts obtained from the multi-objective genetic algorithm during turning of CFRP composites. It was found that coated carbide inserts provide lower tool wear and surface roughness, but higher cutting forces compared to those of uncoated carbide inserts during turning of CFRP composites. Taguchi Analysis was performed to investigate the effects of machining parameters (cutting speed, feed rate and depth of cut) on the output characteristics including cutting force, surface roughness and tool wear. The feed rate was found as the most significant machining parameter in turning of CFRP composites to minimize cutting force and surface roughness using both coated and uncoated carbide inserts. However, feed rate and cutting speed has been found as the most significant machining parameters for coated and uncoated carbide inserts respectively to minimize the tool wear. Regression Analysis has been performed to develop mathematical models for cutting force, surface roughness and tool wear as a function of cutting speed, feed rate and depth of cut. Higher R2 values and well fitted regression lines of normal probability plots in regression analysis indicate that the coefficients of mathematical models are statistically significant. The significance of this study is to emphasize the differences of performances between coated and uncoated carbide inserts during turning of CFRP composites in terms of cutting force, tool wear and surface roughness with the combination of different machining parameters (cu

关键词： CFRP composites Taguchi Analysis multi-objective genetic algorithm

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A New multi-objective genetic algorithm for Feature Subset Selection in Fatigue Fracture Image Identification

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JOURNAL OF COMPUTERS 2010年第7期5卷 1105-1111页

作者： Li Ling Li Ming Lu YuMing Zhang YongLiang Nanjing Univ Aeronaut & Astronaut Coll Automat Nanjing 210016 Jiangsu Peoples R China Nanchang Hangkong Univ Key Lab Nondestruct Testing Minist Educ Nanchang 330063 Jiangxi Peoples R China

Feature subset selection is the most important and difficult task in the field of fatigue fracture image identification. In this paper, a new method which is hybrid of linear prediction, called LP-Based multi-objective genetic algorithms (LP-MOGA) is proposed for fatigue fracture feature subset selection. In LP-MOGA, predicted new solutions with elite solutions by liner prediction to improve the local search ability. For fatigue fracture identification, texture character and fractal dimension feature are extracted for original features;and then, feature subset selection is performed by LP-MOGA, in which, the objective functions minimize error identification rate, undetected identification rate and selected featured number;at last, the identification is executed by quadratic distance classifier. Compared with other methods, the experiment results of actual data demonstrate the presented algorithm is effective.

关键词： multi-objective genetic algorithm Liner Prediction Feature Extraction Feature Subset Selection Fatigue Fracture Identification

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