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Parallel multi-objective genetic algorithm for Short-Term Economic Environmental Hydrothermal Scheduling

ENERGIES 2017年第2期10卷 163页

作者： Feng, Zhong-Kai Niu, Wen-Jing Zhou, Jian-Zhong Cheng, Chun-Tian Qin, Hui Jiang, Zhi-Qiang Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Peoples R China Dalian Univ Technol Inst Hydropower Syst & Hydroinformat Dalian 116024 Peoples R China

With the increasingly serious energy crisis and environmental pollution, the short-term economic environmental hydrothermal scheduling (SEEHTS) problem is becoming more and more important in modern electrical power systems. In order to handle the SEEHTS problem efficiently, the parallel multi-objective genetic algorithm (PMOGA) is proposed in the paper. Based on the Fork/Join parallel framework, PMOGA divides the whole population of individuals into several subpopulations which will evolve in different cores simultaneously. In this way, PMOGA can avoid the wastage of computational resources and increase the population diversity. Moreover, the constraint handling technique is used to handle the complex constraints in SEEHTS, and a selection strategy based on constraint violation is also employed to ensure the convergence speed and solution feasibility. The results from a hydrothermal system in different cases indicate that PMOGA can make the utmost of system resources to significantly improve the computing efficiency and solution quality. Moreover, PMOGA has competitive performance in SEEHTS when compared with several other methods reported in the previous literature, providing a new approach for the operation of hydrothermal systems.

关键词： parallel computing economic environmental hydrothermal scheduling multi-objective optimization multi-objective genetic algorithm constraint handling method

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Potential for optimization of energy consumption and costs in saffron production in central Iran through data envelopment analysis and multi-objective genetic algorithm

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ENVIRONMENTAL PROGRESS & SUSTAINABLE ENERGY 2022年第5期41卷 e13857-e13857页

作者： Saeidi, Elham Dehkordi, Amin Lotfalian Nabavi-Pelesaraei, Ashkan Shahrekord Univ Fac Agr Dept Mech Engn Biosyst POB 115 Shahrekord *** Iran Razi Univ Fac Agr Dept Mech Engn Biosyst Kermanshah Iran

Technical management of agricultural units plays an important role in increasing the yield, energy efficiency, and decreasing the production costs. Based on that, the present study aimed to evaluate and optimize the technical and economic efficiency in Saffron farms in the 2019-20 cropping season in Iran. Required data were collected from 70 Saffron farms through interviews and questionnaires and were analyzed and compared using two optimization methods including data envelopment analysis (DEA), and multi-objective genetic algorithm (MOGA). Based on the results related to the energy section, the total energy input was obtained as 43,578 MJ ha(-1) before any optimization, while it was determined as 36,033 and 36,910 MJ ha(-1) after optimization using DEA, and MOGA, respectively. Also DEA and MOGA methods improved the energy ratio index (ER) (0.002) by 50, and 159%, respectively. Results related to the economic section showed that the total production costs were mitigated from 1260 $ ha(-1) to 863.5 and 1069 $ ha(-1) after optimization by DEA, and MOGA, respectively. After optimization of revenue (using MOGA method), and total costs (using MOGA, and DEA), the benefit cost ratio index was improved from 1.43 to 2.09 (using DEA), and 3.3 (using MOGA). Consequently, MOGA optimization method showed better results compared to DEA in both energy and economic sections.

关键词： benefit cost ratio data envelopment analysis energy ratio multi-objective genetic algorithm saffron production

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Shape optimization approach for cambered otter board using neural network and multi-objective genetic algorithm

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APPLIED OCEAN RESEARCH 2020年 100卷 102148-102148页

作者： You, Xinxing Hu, Fuxiang Dong, Shuchuang Takahashi, Yuki Shiode, Daisuke Tokyo Univ Marine Sci & Technol Minato Ku 4-5-7 Konan Tokyo 1088477 Japan Univ Tokyo Inst Ind Sci Tokyo 1538505 Japan Hokkaido Univ Fac Fisheries Sci 3-1-1 Minato Cho Hakodate Hokkaido 0418611 Japan

The shape optimization approach of the cambered otter board has been performed by the integration of the neural network model and the multi-objective genetic algorithm (MOGA). Because the excellent performance of an otter board is expressed by great lift and less drag force, in this study the lift and drag coefficients were chosen as objective functions to obtain the optimal otter board. The Bezier curve represented the cambered otter board as a simple structure with five control points resulting in the six coordinates, which were adopted as the design variables. The hydrodynamic characteristics of twenty-five otter board models were calculated in a two-dimension computational fluid dynamics (CFD) analysis at an attack angle of 20 degrees. The implicit fitness function in the MOGA algorithm was then obtained by the backpropagation neural network model based on the estimated results of CFD calculation. A set of thirty optimal otter board models were extracted in the optimal solutions of the MOGA, and two optimal models were selected to verify the feasibility of the approach by hydrodynamic and visualization experiments with a comparative hyper-lift trawl door (HLTD). The model 1 showed greatest lift-todrag ratio before the attack angle of 30 degrees as a high lift-to-drag ratio otter board, and the model 2 showed a large lift coefficient and lift-to-drag ratio than the HLTD before the attack angle of 25 degrees as a large lift force otter board. Through the flow distribution around the model 2, it is observed that the flow separation on the suction side is prevented as a result of less drag owing to the modified shape. In summary, the shape optimization approach is efficient in designing optimal otter board to satisfy supposed needs in otter trawling.

关键词： Cambered otter board Hydrodynamic characteristics Lift-to-drag ratio Neural network multi-objective genetic algorithm PIV

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Optimization of hybrid cable networks with dampers and cross-ties for vibration control via multi-objective genetic algorithm

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2022年第0期166卷 108454-108454页

作者： Di, Fangdian Sun, Limin Chen, Lin Tongji Univ Dept Bridge Engn Shanghai 200092 Peoples R China Tongji Univ State Key Lab Disaster Reduct Civil Engn Shanghai 200092 Peoples R China Shanghai Qizhi Inst Shanghai 200092 Peoples R China

For long-span cable-stayed bridges, interconnecting adjacent cables using cross-ties and connecting each cable to bridge deck become a promising solution for cable vibration mitigation, leading to hybrid cable networks. However, optimizing the system parameters and configuration for best vibration control effects is still challenging. Therefore, this study proposes an optimization method based on three important ingredients, i.e., a general and efficient numerical model of cable networks, a reasonable definition of the optimization objectives, and a multi-objective optimization method. The cable network is modeled using the component mode synthesis method with modal expansion and quasi-static correction, the optimization objectives consider both multi-mode damping effects and stiffness enhancement, and the multi-objective genetic algorithm is then employed. The proposed method is first verified by comparing with analytical methods when applying to relatively simple cable-damper systems. Subsequently, a five-cable network is studied using the method for optimal design of cross-tie and damper parameters. Furthermore, the method is applied to optimize cross-ties and dampers for the eight longest cables in the Sutong Bridge, considering different numbers, locations and anchoring conditions of the cross-ties. The results have demonstrated the effectiveness of the proposed method in dealing with real-world large-scale cable networks. It is shown that the optimization can ensure that vibrations in local modes of the cable network are dominated by cable segments with a damper installed in the span and hence are sufficiently damped. Besides, damping ratios of hybrid cable networks are shown to be sensitive to the cross-tie location and parameters while they are relatively insensitive to the damper coefficients.

关键词： Parameter optimization Hybrid cable networks Dampers Cross-ties multi-objective genetic algorithm

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Application of multi-objective genetic algorithm Based Simulation for Cost-Effective Building Energy Efficiency Design and Thermal Comfort Improvement

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FRONTIERS IN ENERGY RESEARCH 2018年第APR期6卷

作者： Lin, Yaolin Yang, Wei Wuhan Univ Technol Sch Civil Engn & Architecture Wuhan Peoples R China Victoria Univ Coll Engn & Sci Melbourne Vic Australia

Building design following the energy efficiency standards may not achieve the optimal performance in terms of investment cost, energy consumption and thermal comfort. In this paper, an improved multi-objective genetic algorithm (NSGA-II) is combined with building simulation to assist building design optimization for five selected cities located in the hot summer and cold winter region in China. The trade-offs between the annual energy consumption (AEC) and initial construction cost, as well as between life cycle cost (LCC) and number of thermal discomfort hours, were explored. Sensitivity analysis of various design parameters on building energy consumption is performed. The optimizations predicted AEC reduction of 29.08% on average, as compared to a reference building designed following the standard, and 38.6% with 3.18% more cost on the initial investment. New values for a number of building design parameters are recommended for the revision of relevant building energy efficiency standard.

关键词： building design optimization energy efficiency design standard life cycle cost thermal comfort multi-objective genetic algorithm

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Integrating the deep learning and multi-objective genetic algorithm to the reloading pattern optimization of HPR1000 reactor core

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NUCLEAR ENGINEERING AND DESIGN 2024年 428卷

作者： Butt, Muhammad Kamran Cao, Liangzhi Wan, Chenghui Lei, Kaihui Khan, Izat Xi An Jiao Tong Univ Sch Nucl Sci & Technol 28 Xianning West Rd Xian 710049 Shaanxi Peoples R China

The deep learning and multi-objective genetic algorithm were employed to optimize the fuel reloading pattern for HPR1000, a state-of-the-art nuclear power reactor designed and operated in China, also known as Hualong-1. In this study, the deep-learning algorithm was applied to establish the rapid evaluator for fuel-reloading patterns, for which the random samples between fuel-reloading patterns and corresponding key core parameters were generated by our home-developed nuclear-design code, named Bamboo-C. The advanced machine-learning platform TensorFlow was utilized for the deep-learning model. Then, the multi-objective genetic algorithm was applied to search the optimal fuel-reloading patterns, combined with the rapid evaluator to evaluate the key core parameters in a very short time. The DAKOTA toolkit was employed for optimization using a multi-objective genetic algorithm, for which the cycle length and power-peak factors were selected as the target parameters to establish the fitness function. For verification and application, the above method has been applied to the fuel- reloading optimization for Cycle 2 of HPR1000 operated in China. The optimization pattern results in an extension of the cycle length by about 21 EFPD (Effective Full Power Day), keeping all the key safety parameters satisfying corresponding safety criteria.

关键词： Deep-learning algorithm multi-objective genetic algorithm HPR1000 Bamboo-C

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Optimization of Halbach magnetic roller for eddy current separation based on the response surface method and multi-objective genetic algorithm

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JOURNAL OF CLEANER PRODUCTION 2021年 278卷 123531-123531页

作者： Bin, Cao Yi, Yuan Yerbol, Arman Lei, Feng Zhou Zongqiang Wang Tiansheng Qiang, Wang Northeastern Univ Sch Met Shenyang 110819 Peoples R China Northeastern Univ Minist Educ Key Lab Electromagnet Proc Mat Shenyang 110819 Peoples R China

Performance optimization of the magnetic roller of eddy current separator is an issue of great significance in the recycling industry. In this study, the Halbach array is proposed for the first time to be used in the eddy current separator. And the new concepts of area field intensity and magnet efficiency density are proposed to quantitatively evaluate the magnetic field intensity around the magnetic roller surface and the utilization efficiency of the magnet respectively. Then the effects of various structural parameters of the Halbach magnetic roller on the area field intensity and magnet efficiency density were systematically investigated by employing finite element analysis and response surface methodology. The results showed that the regression models of the area field intensity (R-2 = 0.9846, Pred.R-2 Y = 0.9420) and magnet efficiency density (R-2 = 0.9946, Pred.R-2 = 0.9782) are extremely significant and have good prediction capability. The magnetic roller can form a stronger magnetic field by selecting a larger magnetic block, and the utilization efficiency of the magnet is higher when the magnetic block's shape tends to be flat or has larger curvature. Furthermore, a multi-objective optimization model with the area field intensity, magnet efficiency density, and magnetic pole number is established, and a multi-objective genetic algorithm is applied to get the optimal combinations of various structural parameters. It was found that the points of the Pareto-optimal solution are concentrated on a spatial surface, which reflects the trade-off relationship among the three objectives. It also shows that the performance parameters of many Pareto-optimal solutions are superior to that of previous studies and the eddy current separators found in the vicinity. The proposed method framework and the results are useful for the design and optimization of the magnetic roller, which will improve the separation efficiency of eddy current separation. (C) 2020 Elsevier Ltd.

关键词： Halbach magnetic roller Finite element analysis Response surface methodology multi-objective genetic algorithm Eddy current separator

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Energy Disaggregation Using multi-objective genetic algorithm Designed Neural Networks

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ENERGIES 2022年第23期15卷 9073页

作者： Laouali, Inoussa Gomes, Isaias Ruano, Maria da Graca Bennani, Saad Dosse Fadili, Hakim El Ruano, Antonio Univ Algarve Fac Sci & Technol DEEI P-8005294 Faro Portugal Sidi Mohamed Ben Abdellah Univ Fac Sci & Technol SIGER Fez POB 2202 Fes Morocco Univ Lisbon IDMEC Inst Super Tecn P-1950044 Lisbon Portugal Univ Evora ICT P-7002554 Evora Portugal Univ Coimbra Fac Sci & Technol CISUC P-3030290 Coimbra Portugal Sidi Mohamed Ben Abdellah Univ Fac Sci & Technol LIPI Fez POB 5206 Fes Morocco

Energy-saving schemes are nowadays a major worldwide concern. As the building sector is a major energy consumer, and hence greenhouse gas emitter, research in home energy management systems (HEMS) has increased substantially during the last years. One of the primary purposes of HEMS is monitoring electric consumption and disaggregating this consumption across different electric appliances. Non-intrusive load monitoring (NILM) enables this disaggregation without having to resort in the profusion of specific meters associated with each device. This paper proposes a low-complexity and low-cost NILM framework based on radial basis function neural networks designed by a multi-objective genetic algorithm (MOGA), with design data selected by an approximate convex hull algorithm. Results of the proposed framework on residential house data demonstrate the designed models' ability to disaggregate the house devices with excellent performance, which was consistently better than using other machine learning algorithms, obtaining F1 values between 68% and 100% and estimation accuracy values ranging from 75% to 99%. The proposed NILM approach enabled us to identify the operation of electric appliances accounting for 66% of the total consumption and to recognize that 60% of the total consumption could be schedulable, allowing additional flexibility for the HEMS operation. Despite reducing the data sampling from one second to one minute, to allow for low-cost meters and the employment of low complexity models and to enable its real-time implementation without having to resort to specific hardware, the proposed technique presented an excellent ability to disaggregate the usage of devices.

关键词： non-intrusive load monitoring (NILM) energy disaggregation neural networks multi-objective genetic algorithm low frequency power data convex hull algorithms

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Intelligent optimization of tamping parameters using discrete element and radial basis function-multi-objective genetic algorithm (RBF-MOGA)

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AUTOMATION IN CONSTRUCTION 2024年第PartA期168卷

作者： Shi, Shunwei Wang, Ji Gao, Liang Zhang, Yanan Xiao, Yixiong Ding, Jiaxuan Beijing Jiaotong Univ Sch Civil Engn Beijing 100044 Peoples R China Beijing Jiaotong Univ Beijing Key Lab Track Engn Beijing 100044 Peoples R China

Tamping can effectively recover the geometry of a ballasted track. Nevertheless, it can decrease the mechanical properties of ballast bed. To mitigate the damage caused by tamping, this paper developed an intelligent optimization method for tamping parameters using the DEM-RBF-MOGA. This intelligent optimization method enables automatic simulation and simultaneous data transmission. Additionally, it can reveal the interactions among tamping parameters. Hence, the developed intelligent method has higher accuracy and efficiency than traditional methods. Subsequently, the sensitive characteristics of the ballast bed for tamping parameters were investigated, and the corresponding objective functions for optimizing were developed. Finally, a surrogate model was constructed and optimal tamping parameters were proposed. The results indicate that the objective functions vary nonlinearly and irregularly with the tamping parameters. The optimal vibration frequency, vibration amplitude, and squeezing force of tamping operation for ballast bed with different mechanical properties can be easily determined using this intelligent method. This paper provides effective guidelines for improving the maintenance of ballasted tracks.

关键词： Railway maintenance Ballasted track Tamping parameters Intelligent optimization multi-objective genetic algorithm

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POLYNOMIAL NARX MODEL STRUCTURE OPTIMIZATION USING multi-objective genetic algorithm

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2012年第10B期8卷 7341-7362页

作者： Loghmanian, Sayed Mohammad Reza Yusof, Rubiyah Khalid, Marzuki Ismail, Fatimah Sham Univ Teknol Malaysia Ctr Artificial Intelligence & Robot Kuala Lumpur Malaysia

Model structure selection is an important step in system identification which involves the selection of variables and terms of a model. The important issue is choosing a compact model representation where only significant terms are selected among all the possible ones beside good performance. This research explores the use of multi-objective optimization to minimize the complexity of a model structure and its predictive error simultaneously. The model structure representation is a polynomial non-linear autoregressive with exogenous input model. A new modified elitist non-dominated sorting genetic algorithm using clustered crowding distance (CCD) is proposed to find the exact model among non-dominated solutions, using some simulated examples which generate data set by mathematical equations. Simulation results demonstrated that the proposed algorithm can find the correct model with exact terms and values in all cases of problem. Furthermore, the effectiveness of the proposed algorithm is also studied by applying to the real process data sets, and the final model can be chosen from a set of non-dominated solutions referred as Pareto optimal front. The results show that the proposed clustered CD has better performance compared with the basic CD method.

关键词： NARX multi-objective genetic algorithm NSGA-II System identification Model structure selection Clustered crowding distance

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