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IEEE International Conference on Mechatronics and Automation

作者： Chang, Wei Wang, Jiuhe Chen, Qili Beijing Informat Sci & Technol Univ Beijing Peoples R China

ISBN: (纸本)9781509023967

This paper proposes a multi-objective optimization design method with the discrete variables for synchronous buck converter. This method defines three objective factors: minimize cost, minimize area and maximize efficiency, and a multi-objective optimization problem can be drawn with the objective function of assessment models of cost, area and loss and the constraint of the component database built by the parameter of MOSFETs, inductors and capacitors. This paper uses the nsga-ii algorithm to process the problem of a multi-objective optimization with discrete variables. The nsga-ii algorithm can find the pareto optimal solution of a very large solution space which has hundreds of thousands feasible combinations of components, and the designers can make the reasonable choice of components by performing the trade-off analysis for the optimal solution when selecting the components. The simulation results demonstrate the feasibility of multi-objective optimization design method in this paper.

关键词： Synchronous Buck Converter Component Database nsga-ii algorithm the Pareto Optimal Solution Trade-off Analysis

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Multi-Radar Cooperative Task Planning using nsga-ii algorithm 21

Multi-Radar Cooperative Task Planning using NSGA-II Algorith...

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21st IEEE International Conference on Software Quality, Reliability and Security (QRS)

作者： Liu, Xiang Zhang, Chao Cao, Yanjun Han, Li Wu, Zhiming China Elect Technol Corp Res Inst 54 Shijiazhuang Hebei Peoples R China China Univ Geosci Wuhan Peoples R China

ISBN: (纸本)9781665478366

Adopting multi-radar cooperative plan technology to realize the unified tasks planning can improve the efficiency of existing radars. By studying the characteristics of multi-radar cooperative planning tasks, the nsga-ii algorithm adopted in this paper can quickly complete multi-radar cooperative task planning. The result has the characteristics of uniform data distribution, high convergence and high robustness, and has better task optimization effect. It is a good idea to solve multi-radar for collaborative tasks planning.

关键词： nsga-ii algorithm collaborative task planning Multi-objective optimization

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Multi-objective PID Controller Based on nsga-ii algorithm with Application to Main Steam Temperature Control

Multi-objective PID Controller Based on NSGA-II Algorithm wi...

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International Conference on Artificial Intelligence and Computational Intelligence

作者： Guan Xiang-zhong Shanghai Univ Elect Power Shanghai Peoples R China

ISBN: (纸本)9780769538167

PID controller is widely used for main steam temperature control of boiler unit in thermal power plant. To avoid the drawback of current PID design methods, this paper presents a new design method for multi-objective PID controller to synthetically consider system requirement in speediness, reliability and robustness. nsga-ii algorithm with the elite strategy is applied to the parameter optimization design. The simulation results on an actual main steam temperature control system indicate that, the multi-objective PID controller designed by presented method, can improve the dynamic performance of main steam temperature control system, with good robustness and anti-interference ability.

关键词： boiler main steam temperature control multi-objective PID controller nsga-ii algorithm Pareto-optimal solutions

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Distributed Energy Resources Network Connection Considering Reliability Optimization using a nsga-ii algorithm 11

Distributed Energy Resources Network Connection Considering ...

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11th IEEE International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG)

作者： Pombo, A. Vieira Murta-Pina, J. Pires, V. Fernao Inst Politecn Setubal Elect Engn Dept ESTSetubal Setubal Portugal Univ Nova Lisboa CTS UNINOVA FCT Caparica Portugal INESC ID Lisboa ESTSetubal Inst Politecn Setubal Setubal Portugal

ISBN: (纸本)9781509049646

Distributed Energy Resources (DER) has been widely introduced in distribution networks in response to the increase of environmental awareness of the consumers. The benefits with the use of DER are increased with network reconfiguration, but in some countries exists the impossibility of island operation as well as the obligation to buy all the electricity generated. These limitations, in a network fault situation, causes a waste of resources, because of the unavailability of the DER. On other hand, the location of DER connection under the context of the improvement of the reliability indexes has not been studied. Thus, this paper will propose a multi objective optimization of the location of DER connection considering the switching devices placement to increase network reliability and availability of DER, minimizing at the same time the investment in equipment in a no island operation environment. For the resolution of the proposed formulation, it will be used the multi objective algorithm nsga-ii (Fast Non-dominated Sorting Genetic algorithm). The formulation of the problem also considers a composite index made up of DER and interruption duration. This approach is applied to a real utility distribution network, with the results presented and discussed.

关键词： Multiobjective Optimization Switching Devices nsga-ii algorithm Distributed Energy Resources No-Island Operation

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Multiobjective optimization design of high frequency transformer based on nsga-ii algorithm 19

Multiobjective optimization design of high frequency transfo...

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19th IEEE International Conference on Mechatronics and Automation (IEEE ICMA)

作者： Wang, Chunjie Han, Wenkai Chen, Peng Song, Jinchuan Yuan, Songwei Tianjin Univ Technol Tianjin Key Lab Control Theory & Applicat Compli Coll Elect Engn & Automat Binshui Xidao Extens 391 Tianjin 300384 Peoples R China Tianjin Keyvia Elect Co Ltd Tianjin 300392 Peoples R China

ISBN: (数字)9781665408530

ISBN: (纸本)9781665408530;9781665408523

Aiming at the optimization design of high frequency transformer in low-voltage high-current power electronic converter, the volume and efficiency of high frequency transformer are taken as the optimization objectives, the calculation methods of magnetic core and winding loss of high-frequency transformer are analyzed. On this basis, a twodimensional optimization model is established with saturated magnetic induction intensity and current density as optimization variables. Genetic algorithm has certain advantages in solving nonlinear multiobjective optimization problems. In this paper, nsga-ii algorithm is used to optimize the design of high frequency transformers with ferrite, amorphous alloy and nanocrystalline core materials. By comparing the optimization parameters of the simulation results, the superiority of nanocrystalline materials in the design of high frequency transformer is verified, due to its high saturation magnetic induction strength and high permeability, nanocrystalline materials have better comprehensive performance than ferrite and amorphous alloy materials in high-frequency and highpower applications.

关键词： Multiobjective optimization High frequency transformer Magnetic core material nsga-ii algorithm

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Multi-objective Optimization Scheduling Model Based on nsga-ii algorithm 3

Multi-objective Optimization Scheduling Model Based on NSGA-...

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3rd IEEE International Conference on Electronics and Communication Engineering (ICECE)

作者： Bian, Ruifeng Tan, Wenyi Li, Yilun Hou, Yichen Wuhan Univ Technol Sch Automat Wuhan Peoples R China

ISBN: (纸本)9781728192581

The preparation of steelmaking-continuous casting-hot rolling production planning is a complex combinational optimization problem with multiple objectives and constraints. Based on the inductive characteristics and constraints, a multi-objective optimization model for integrated production of steelmaking, continuous casting, and hot rolling is established. In this paper, the 2nd generation of non-dominated sorting genetic algorithm (nsga-ii) is employed to make converters, continuous casters and hot mill have the lowest idle capacity, and the idle capacity of each heat, cast, and rolling unit are respectively summed as the objective function in the multiobjective optimization. According to the process features of steelmaking, continuous casting and hot rolling, the constraints in each production process is enumerated. Plug-in coding method is designed, the nsga-ii algorithm is adopted to solve the pareto front of the multi-objective optimization, and the lowest production time solution in the pareto front is selected as the final production plan. Finally, a simulation analysis is conducted on the actual project to verify the effectiveness of the algorithm, and meanwhile prove its reference value when applied to actual production scheduling problems.

关键词： component nsga-ii algorithm plug-in coding pareto front multi-objective optimization scheduling model

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Multi- objective modeling and optimization of dissimilar laser welding by integrating an artificial intelligence predictive model with nsga-ii algorithm

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MATERIALS TODAY COMMUNICATIONS 2024年 40卷

作者： Aghaei, Hosein Larki, Hossein Ashtari Taqizadeh, Milad Aoudia, Mouloud Aich, Walid Kolsi, Lioua Dehkordi, Mohammad Hossein Razavi Isfahan Univ Technol Dept Mech Engn Esfahan Iran Islamic Azad Univ Dept Mech Engn Najafabad Branch Najafabad Iran Northern Border Univ Coll Engn Dept Ind Engn Ar Ar 73213 Saudi Arabia Univ Hail Coll Engn Dept Mech Engn Hail 81451 Saudi Arabia Univ Monastir Lab Meteorol & Energy Syst Monastir 5000 Tunisia Islamic Azad Univ Aerosp & Energy Convers Res Ctr Najafabad Branch Najafabad Iran

This study presents a pioneering approach in laser welding of two dissimilar materials by integrating an Artificial Intelligence (AI)-based on predictive model with the Non-dominated Sorting Genetic algorithm ii (nsga-ii) for optimizing key performance characteristics of Nickel-based alloy and duplex 2205 stainless steel. Focusing on four primary input variables including laser power, welding speed, focal distance and deviation, the research aims to predict and optimize the responses including temperature field adjacent to the melt pool, penetration depth, and tensile strength of the joint according to the experimental results. The developed AI model first accurately forecasts these characteristics based on the inputs. This predictive accuracy is critical in defining the optimal target values. Considering the multidimensional nature of the problem, where enhancing one characteristic could compromise another, the study employs a Multi-Objective Optimization (MOO) strategy. This is where the innovative integration with nsga-ii becomes pivotal. Renowned for its efficiency in navigating multiple, potentially conflicting objectives, nsga-ii assists in achieving a balanced optimization of all target parameters. This method is adept at considering the complex interdependencies among various characteristics. The novelty of this work lies in its unique combination of AI for prediction and MOO for optimization and it is for the first time that machine learning is applied on this kind of alloys with four features and four targets.

关键词： Nickel-based alloy Duplex 2205 steel Laser welding Artificial intelligence nsga-ii algorithm

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Research on Multi-Objective Process Parameter Optimization Method in Hard Turning Based on an Improved nsga-ii algorithm

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PROCESSES 2024年第5期12卷 950页

作者： Zhang, Zhengrui Wu, Fei Wu, Aonan Wuhan Univ Technol Sch Mech & Elect Engn Wuhan 430070 Peoples R China

To address the issue of local optima encountered during the multi-objective optimization process with the Non-dominated Sorting Genetic algorithm ii (nsga-ii) algorithm, this paper introduces an enhanced version of the nsga-ii. This improved nsga-ii incorporates polynomial and simulated binary crossover operators into the genetic algorithm's crossover phase to refine its performance. For evaluation purposes, the classic ZDT benchmark functions are employed. The findings reveal that the enhanced nsga-ii algorithm achieves higher convergence accuracy and surpasses the performance of the original nsga-ii algorithm. When applied to the machining of the high-hardness material 20MnCrTi, four algorithms were utilized: the improved nsga-ii, the conventional nsga-ii, nsga-iiI, and MOEA/D. The experimental outcomes show that the improved nsga-ii algorithm delivers a more optimal combination of process parameters, effectively enhancing the workpiece's surface roughness and material removal rate. This leads to a significant improvement in the machining quality of the workpiece surface, demonstrating the superiority of the improved algorithm in optimizing machining processes.

关键词： multi-objective optimization hard turning nsga-ii algorithm improved algorithm process parameters machining process

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Application of the nsga-ii algorithm and Kriging Model to Optimise the Process Parameters for the Improvement of the Quality of Fresnel Lenses

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POLYMERS 2023年第16期15卷 3403-3403页

作者： Chang, Hanjui Sun, Yue Wang, Rui Lu, Shuzhou Shantou Univ Coll Engn Dept Mech Engn Shantou 515063 Peoples R China Shantou Univ Intelligent Mfg Key Lab Minist Educ Shantou 515063 Peoples R China

The Fresnel lens is an optical system consisting of a series of concentric diamond grooves. One surface of the lens is smooth, while the other is engraved with concentric circles of increasing size. Optical interference, diffraction, and sensitivity to the angle of incidence are used to design the microstructure on the lens surface. The imaging of the optical surface depends on its curvature. By reducing the thickness of the lens, light can still be focused at the same focal point as with a thicker lens. Previously, lenses, including Fresnel lenses, were made of glass due to material limitations. However, the traditional grinding and polishing methods for making Fresnel lenses were not only time-consuming, but also labour-intensive. As a result, costs were high. Later, a thermal pressing process using metal moulds was invented. However, the high surface tension of glass caused some detailed parts to be deformed during the pressing process, resulting in unsatisfactory Fresnel lens performance. In addition, the complex manufacturing process and unstable processing accuracy hindered mass production. This resulted in high prices and limited applications for Fresnel lenses. These factors prevented the widespread use of early Fresnel lenses. In contrast, polymer materials offer advantages, such as low density, light weight, high strength-to-weight ratios, and corrosion resistance. They are also cost effective and available in a wide range of grades. Polymer materials have gradually replaced optical glass and other materials in the manufacture of micro-optical lenses and other miniaturised devices. Therefore, this study focuses on investigating the manufacturing parameters of Fresnel lenses in the injection moulding process. We compare the quality of products obtained by two-stage injection moulding, injection compression moulding, and IMD (in-mould decoration) techniques. The results show that the optimal method is IMD, which reduces the nodal displacement on the Fresnel

关键词： Fresnel lens residual stress transmittance nsga-ii algorithm Kriging model non-destructive analysis

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A multi-objective optimization approach for battery thermal management system based on the combination of BP neural network prediction and nsga-ii algorithm

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JOURNAL OF ENERGY STORAGE 2024年 99卷

作者： Ye, Lisheng Li, Caisheng Wang, Changhong Zheng, Junxin Zhong, Kaiwei Wu, Tingting Guangdong Univ Technol Sch Mat & Energy Guangzhou 51006 Peoples R China

Numerical computation and repeated experiments are the main optimization methods used in traditional battery thermal management systems (BTMs) to obtain a better structure by changing a single variable, which incurs higher time cost. In this paper, a prediction model based on back propagation neural network (BPNN) is established by combining the data mining approach, and then the Non-dominated Sorting Genetic algorithm (nsga-ii) is used for the optimization of BTMs. In this study, the battery spacing, liquid cooling tube diameter, and inlet velocity are adopted as three independent variables of the surrogate model, and the maximum temperature (Tmax), T max ), maximum standard temperature difference (Delta Ttsd) Delta T tsd ) and system maximum pressure drop (Delta Pmax) Delta P max ) are used as dependent variables for multi-objective optimization. The prediction results revealed that the deep learning trained BPNN model is a high fidelity model with the correlation coefficient R reaching 0.99988 and the mean square error (MSE) decreasing to 9.5185E-6 during the iteration process. The results of the algorithm showed that T max decreased by 0.22 K, Delta T tsd decreased by 0.18 K, and Delta P max decreased by 95.7 Pa compared to the original design. Among them, the Delta P max decreases significantly, and 55.17 % of the system pressure drop decreases compared to initial structure. Combining the predictive model with the algorithm not only improves the efficiency of designing the BTMs, but also significantly improves the cooling efficiency, which results in energy savings and improved economics.

关键词： BP neural network nsga-ii algorithm Battery thermal management system Multi-objective optimization

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