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Improved non-dominated sorting genetic algorithm (NSGA)-II in multi-objective optimization studies of wind turbine blades 认领 引用 被引量：33
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作者 王珑 王同光 罗源 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI 2011年第6期739-748,共10页
The non-dominated sorting genetic algorithm （NSGA） is improved with the controlled elitism and dynamic crowding distance. A novel multi-objective optimization algorithm is obtained for wind turbine blades. As an exa... The non-dominated sorting genetic algorithm （NSGA） is improved with the controlled elitism and dynamic crowding distance. A novel multi-objective optimization algorithm is obtained for wind turbine blades. As an example, a 5 MW wind turbine blade design is presented by taking the maximum power coefficient and the minimum blade mass as the optimization objectives. The optimal results show that this algorithm has good performance in handling the multi-objective optimization of wind turbines, and it gives a Pareto-optimal solution set rather than the optimum solutions to the conventional multi objective optimization problems. The wind turbine blade optimization method presented in this paper provides a new and general algorithm for the multi-objective optimization of wind turbines. 展开更多
关键词 wind turbine multi-objective optimization Pareto-optimal solution non-dominated sorting genetic algorithm （NSGA）-II
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An Optimization Approach for Convolutional Neural Network Using Non-Dominated Sorted Genetic Algorithm-Ⅱ 认领 引用
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作者 Afia Zafar Muhammad Aamir +6 位作者 Nazri Mohd Nawi Ali Arshad Saman Riaz Abdulrahman Alruban Ashit Kumar Dutta Badr Almutairi Sultan Almotairi 《Computers, Materials & Continua》 SCIE EI 2023年第3期5641-5661,共21页
In computer vision,convolutional neural networks have a wide range of uses.Images representmost of today’s data,so it’s important to know how to handle these large amounts of data efficiently.Convolutional neural ne... In computer vision,convolutional neural networks have a wide range of uses.Images representmost of today’s data,so it’s important to know how to handle these large amounts of data efficiently.Convolutional neural networks have been shown to solve image processing problems effectively.However,when designing the network structure for a particular problem,you need to adjust the hyperparameters for higher accuracy.This technique is time consuming and requires a lot of work and domain knowledge.Designing a convolutional neural network architecture is a classic NP-hard optimization challenge.On the other hand,different datasets require different combinations of models or hyperparameters,which can be time consuming and inconvenient.Various approaches have been proposed to overcome this problem,such as grid search limited to low-dimensional space and queuing by random selection.To address this issue,we propose an evolutionary algorithm-based approach that dynamically enhances the structure of Convolution Neural Networks(CNNs)using optimized hyperparameters.This study proposes a method using Non-dominated sorted genetic algorithms(NSGA)to improve the hyperparameters of the CNN model.In addition,different types and parameter ranges of existing genetic algorithms are used.Acomparative study was conducted with various state-of-the-art methodologies and algorithms.Experiments have shown that our proposed approach is superior to previous methods in terms of classification accuracy,and the results are published in modern computing literature. 展开更多
关键词 Non-dominated sorted genetic algorithm convolutional neural network hyper-parameter optimization
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Techno-economic co-optimization of CO2enhanced oil recovery strategies in a tight oil reservoir using coupled improved evolutionary algorithm and machine learning framework 认领 引用
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作者 Shu-Qin Wen Bing Wei +2 位作者 Jun-Yu You Nan-Jiang Leng William Ampomah 《Petroleum Science》 SCIE EI CAS CSCD 2026年第5期2639-2654,共16页
Massive carbon dioxide(CO2)emissions drive climate change.Injecting CO2into unconventional reservoirsachieves both enhanced oil recovery(EOR)and geological sequestration.However,simultaneously optimizing oil exc... Massive carbon dioxide(CO2)emissions drive climate change.Injecting CO2into unconventional reservoirsachieves both enhanced oil recovery(EOR)and geological sequestration.However,simultaneously optimizing oil exchange ratio,CO2storage,and net present value remainschallenging.This study develops an integrated machine learning(ML)-based framework for multi-objective optimization of CO2-EOR.A high-resolution reservoir simulation was constructed from field data,and Latin hypercube sampling generateddiverse scenarios for proxy training.Mantel's test quantified correlations between input parameters and performance metrics,showing that injection strategy strongly controls net present value,whereas geological properties dominate CO2storage.Three ML models—random forest(RF),support vector regression,and artificial neural networks—were evaluated,with RF selected for its superior performance on small datasets.RF was embedded into an improved non-dominatedsorting genetic algorithm II,enhanced with grey difference degree,crowding distance,and adaptive differential evolution to improve diversity and efficiency.Finally,the technique for order preference by similarity to ideal solution ranked Pareto-optimal solutions through integrating oil productivity,storage,and economics.The proposed framework operationalizes simultaneoushigh-efficiency tight oil recovery and field-scale CO2geological storage,delivering quantitative design rules that embed low-carbon practice into upstream operations and advance the energy sector's greenerand sustainable transition. 展开更多
关键词 CO2enhanced oil recovery Multi-objective optimization Improved non-dominated sorting genetic algorithm II Unconventional oil reservoir
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Satellite constellation design with genetic algorithms based on system performance 认领 引用 被引量：3
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作者 Xueying Wang Jun Li +2 位作者 Tiebing Wang Wei An Weidong Sheng 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2016年第2期379-385,共7页
Satellite constellation design for space optical systems is essentially a multiple-objective optimization problem. In this work, to tackle this challenge, we first categorize the performance metrics of the space optic... Satellite constellation design for space optical systems is essentially a multiple-objective optimization problem. In this work, to tackle this challenge, we first categorize the performance metrics of the space optical system by taking into account the system tasks（i.e., target detection and tracking）. We then propose a new non-dominated sorting genetic algorithm（NSGA） to maximize the system surveillance performance. Pareto optimal sets are employed to deal with the conflicts due to the presence of multiple cost functions. Simulation results verify the validity and the improved performance of the proposed technique over benchmark methods. 展开更多
关键词 space optical system non-dominated sorting genetic algorithm（NSGA） Pareto optimal set satellite constellation design surveillance performance
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Planning of DC Electric Spring with Particle Swarm Optimization and Elitist Non-dominated Sorting Genetic Algorithm 认领 引用 被引量：3
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作者 Qingsong Wang Siwei Li +2 位作者 Hao Ding Ming Cheng Giuseppe Buja 《CSEE Journal of Power and Energy Systems》 SCIE EI CSCD 2024年第2期574-583,共10页
This paper addresses the planning problem of parallel DC electric springs (DCESs). DCES, a demand-side management method, realizes automatic matching of power consumption and power generation by adjusting non-critical... This paper addresses the planning problem of parallel DC electric springs (DCESs). DCES, a demand-side management method, realizes automatic matching of power consumption and power generation by adjusting non-critical load (NCL) and internal storage. It can offer higher power quality to critical load (CL), reduce power imbalance and relieve pressure on energy storage systems (RESs). In this paper, a planning method for parallel DCESs is proposed to maximize stability gain, economic benefits, and penetration of RESs. The planning model is a master optimization with sub-optimization to highlight the priority of objectives. Master optimization is used to improve stability of the network, and sub-optimization aims to improve economic benefit and allowable penetration of RESs. This issue is a multivariable nonlinear mixed integer problem, requiring huge calculations by using common solvers. Therefore, particle Swarm optimization (PSO) and Elitist non-dominated sorting genetic algorithm (NSGA-II) were used to solve this model. Considering uncertainty of RESs, this paper verifies effectiveness of the proposed planning method on IEEE 33-bus system based on deterministic scenarios obtained by scenario analysis. 展开更多
关键词 DC distribution network DC electric spring non-dominated sorting genetic algorithm particle swarm optimization renewable energy source
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Suspended sediment load prediction using non-dominated sorting genetic algorithm Ⅱ 认领 引用 被引量：4
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作者 Mahmoudreza Tabatabaei Amin Salehpour Jam Seyed Ahmad Hosseini 《International Soil and Water Conservation Research》 SCIE CSCD 2019年第2期119-129,共11页
Awareness of suspended sediment load (SSL) and its continuous monitoring plays an important role in soil erosion studies and watershed management.Despite the common use of the conventional model of the sediment rating... Awareness of suspended sediment load (SSL) and its continuous monitoring plays an important role in soil erosion studies and watershed management.Despite the common use of the conventional model of the sediment rating curve (SRC) and the methods proposed to correct it,the results of this model are still not sufficiently accurate.In this study,in order to increase the efficiency of SRC model,a multi-objective optimization approach is proposed using the Non-dominated Sorting Genetic Algorithm Ⅱ (NSGA-Ⅱ) algorithm.The instantaneous flow discharge and SSL data from the Ramian hydrometric station on the Ghorichay River,Iran are used as a case study.In the first part of the study,using self-organizing map (SOM),an unsupervised artificial neural network,the data were clustered and classified as two homogeneous groups as 70% and 30% for use in calibration and evaluation of SRC models,respectively.In the second part of the study,two different groups of SRC model comprised of conventional SRC models and optimized models (single and multi-objective optimization algorithms) were extracted from calibration data set and their performance was evaluated.The comparative analysis of the results revealed that the optimal SRC model achieved through NSGA-Ⅱ algorithm was superior to the SRC models in the daily SSL estimation for the data used in this study.Given that the use of the SRC model is common,the proposed model in this study can increase the efficiency of this regression model. 展开更多
关键词 Clustering Neural network Non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ) Sediment rating curve Self-organizing map
A decoupled multi-objective optimization algorithm for cut order planning of multi-color garment 认领 引用
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作者 DONG Hui LYU Jinyang +3 位作者 LIN Wenjie WU Xiang WU Mincheng HUANG Guangpu 《High Technology Letters》 EI CAS 2025年第1期53-62,共10页
This work addresses the cut order planning(COP)problem for multi-color garment production,which is the first step in the clothing industry.First,a multi-objective optimization model of multicolor COP(MCOP)is establish... This work addresses the cut order planning(COP)problem for multi-color garment production,which is the first step in the clothing industry.First,a multi-objective optimization model of multicolor COP(MCOP)is established with production error and production cost as optimization objectives,combined with constraints such as the number of equipment and the number of layers.Second,a decoupled multi-objective optimization algorithm(DMOA)is proposed based on the linear programming decoupling strategy and non-dominated sorting in genetic algorithmsⅡ(NSGAII).The size-combination matrix and the fabric-layer matrix are decoupled to improve the accuracy of the algorithm.Meanwhile,an improved NSGAII algorithm is designed to obtain the optimal Pareto solution to the MCOP problem,thereby constructing a practical intelligent production optimization algorithm.Finally,the effectiveness and superiority of the proposed DMOA are verified through practical cases and comparative experiments,which can effectively optimize the production process for garment enterprises. 展开更多
关键词 multi-objective optimization non-dominated sorting in genetic algorithmsⅡ(NSGAII) cut order planning(COP) multi-color garment linear programming decoupling strategy
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基于SLP与NSGA-II的KF公司通用阀车间布局优化 认领 引用 被引量：1
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作者 陈洪鑫 《科技和产业》 2025年第13期40-50,共11页
针对因KF公司通用阀车间布局不合理而导致物料搬运交叉多、搬运成本高、面积利用率低等问题,构建考虑物料顺、逆流动方向的,以最小化物料搬运成本、最大化非物流关系和车间面积利用率为目标的布局优化模型。运用系统布置设计(SLP)方法... 针对因KF公司通用阀车间布局不合理而导致物料搬运交叉多、搬运成本高、面积利用率低等问题,构建考虑物料顺、逆流动方向的,以最小化物料搬运成本、最大化非物流关系和车间面积利用率为目标的布局优化模型。运用系统布置设计(SLP)方法对车间布局进行优化得到初步布局方案。在传统非支配排序遗传算法(NSGA-II)的基础上,为提高算法初始种群的多样性将SLP方法得到的初步布局方案编码作为初始种群的一部分,将自适应控制策略引入交叉、变异操作中,并加入模拟退火算法。最后使用层次分析法(AHP)对算法得到的一组Pareto最优解集进行优化方案决策。结果表明,此方法能使物料搬运成本减少38.83%,非物流关系增加了44.83%,车间面积利用率优化了19.50%,证明了该模型在车间布局优化时的有效性。 展开更多
关键词 车间布局 多目标优化 NSGA-II(非支配排序遗传算法) SLP(系统布置设计)
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Two-stage two-dimensional force allocation strategy for tunnel boring machines based on a region-reconfigurable thrust system 认领 引用
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作者 Zhe ZHENG Kaihao ZHU +7 位作者 Jiaqi HOU Haibo XIE Lijie JIANG Fulong LIN Lianhui JIA Laikuang LIN Huayong YANG Dong HAN 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2026年第6期583-597,I0035-I0042,共15页
Determining the group forces of the thrust system is essential for trajectory control of tunnel boring machines(TBMs).Existing methods for selecting an optimal solution mainly consider the force variance among groups,... Determining the group forces of the thrust system is essential for trajectory control of tunnel boring machines(TBMs).Existing methods for selecting an optimal solution mainly consider the force variance among groups,while ignoring other constraints,such as uneven segment loading and excessive hydraulic shock.In this study,we develop a more comprehensive and robust framework for force allocation.First,a novel region-reconfigurable hydraulic system is designed,which enforces consistency among the forces acting on each segment.Then on this basis,for the ramping-up tunneling stage,quadratic programming(QP)is used to optimize force uniformity across the spatial dimension.Compared to the on-site allocation result,the improvement in force uniformity reaches up to 32.89%.Moreover,to address the hydraulic shock caused by excessive adjustment to the force,hydraulic compliance is introduced and optimized together with force uniformity using the non-dominated sorting genetic algorithm II(NSGA-II),which outperforms weighted QP by 1.25×106 kN2 in uniformity and 2.86 kN2 in compliance.Analyzing performance in the steady tunneling stage,the service life of the components improves significantly.To avoid a non-existent solution for the thrust force vector,a genetic algorithm-based error tolerance method is developed.Therefore,all deviation rectification commands can be answered with a minor compromise of up to 3%in the fitting accuracy of the thrust force vector.In summary,this framework enhances the adaptability and robustness of the force allocation strategy,providing a reliable foundation for TBM trajectory control. 展开更多
关键词 Tunnel boring machine(TBM) Thrust system Thrust force vector Force allocation Quadratic programming(QP) Non-dominated sorting genetic algorithm II(NSGA-II)
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基于NSGA-II的UPQC多目标PI控制器参数优化研究 认领 引用 被引量：3
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作者 黄雄 吴天杰 +4 位作者 陈锐忠 罗杰 林少佳 宋平平 刘剑 《电机与控制应用》 2025年第3期315-327,共13页
【目的】本文研究了基于非支配排序遗传算法II(NSGA-II)的统一电能质量调节器(UPQC)多目标比例积分(PI)控制器参数优化问题。UPQC作为一种重要的电力质量改善装置,能够有效抑制电网电压波动、谐波及不平衡等问题,但其性能依赖于控制器... 【目的】本文研究了基于非支配排序遗传算法II(NSGA-II)的统一电能质量调节器(UPQC)多目标比例积分(PI)控制器参数优化问题。UPQC作为一种重要的电力质量改善装置,能够有效抑制电网电压波动、谐波及不平衡等问题,但其性能依赖于控制器参数的合理配置。针对传统优化方法难以满足系统的多目标性能需求,且容易陷入局部最优的问题,本文提出了一种基于NSGA-II的多目标优化策略,旨在寻求一种能够同时优化谐波抑制、电压稳定性和动态响应速度的控制器参数配置方案。【方法】本文采用NSGA-II进行多目标优化,该算法通过快速非支配排序和拥挤度计算来实现多目标函数的全局优化。NSGA-II具有良好的全局搜索能力和快速收敛特性,因此优化UPQC控制器的参数时,能够快速而准确地找到最优解。在优化过程中,以谐波抑制、电压稳定性和动态响应速度作为主要优化目标,通过精确调整PI控制器参数,求得最优的控制策略。【结果】通过电网电压补偿仿真和直流、交流侧电压仿真来验证本文所提策略的有效性和准确性。在电网电压补偿仿真中,将本文策略与非线性比例积分-模型预测控制(PI-MPC)策略进行对比,本文所提策略实际补偿电压波形更趋于正弦曲线,且波形较为光滑平顺,谐波含量比非线性PI-MPC策略更小。在直流、交流侧电压仿真中,本文策略比其他策略的调节时间更短且超调量更低,在系统发生扰动时恢复时间更短,具有更强的鲁棒性。【结论】基于NSGA-II的PI控制器参数优化策略能够有效提升UPQC在复杂工况下的性能表现,提高系统的电能质量和响应效率。与传统方法相比,该优化策略不仅提升了电力质量,而且在动态响应过程中表现出更优的稳定性和更快速的调节能力。 展开更多
关键词 参数优化 比例积分控制器 非支配排序遗传算法II 统一电能质量调节器 电能质量
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An improved knowledge-informed NSGA-II for multi-objective land allocation (MOLA) 认领 引用 被引量：16
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作者 Mingjie Song Dongmei Chen 《Geo-Spatial Information Science》 SCIE EI CSCD 2018年第4期273-287,共15页
Multi-objective land allocation(MOLA)can be regarded as a spatial optimization problem that allocates appropriate use to certain land units subjecting to multiple objectives and constraints.This article develops an im... Multi-objective land allocation(MOLA)can be regarded as a spatial optimization problem that allocates appropriate use to certain land units subjecting to multiple objectives and constraints.This article develops an improved knowledge-informed non-dominated sorting genetic algorithm II(NSGA-II)for solving the MOLA problem by integrating the patch-based,edge growing/decreasing,neighborhood,and constraint steering rules.By applying both the classical and the knowledge-informed NSGA-II to a simulated planning area of 30×30 grid,we find that:when compared to the classical NSGA-II,the knowledge-informed NSGA-II consistently produces solutions much closer to the true Pareto front within shorter computation time without sacrificing the solution diversity;the knowledge-informed NSGA-II is more effective and more efficient in encouraging compact land allocation;the solutions produced by the knowledge-informed have less scattered/isolated land units and provide a good compromise between construction sprawl and conservation land protection.The better performance proves that knowledge-informed NSGA-II is a more reasonable and desirable approach in the planning context. 展开更多
关键词 Multi-objective land allocation(MOLA) non-dominated sorting genetic algorithm II(NSGA-II) knowledge-informed rules
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永磁同步电机模型预测转矩控制权重系数设计研究 认领 引用 被引量：1
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作者 李耀华 刘亚辉 +3 位作者 张鑫泉 张茜 黄汉旋 吴步昊 《电机与控制应用》 2026年第1期46-56,共11页
【目的】针对模型预测控制权重系数设计困难的问题,本文采用非支配排序遗传算法II(NSGA-II)和贝叶斯优化算法进行权重系数设计。【方法】基于永磁同步电机(PMSM)模型预测转矩控制(MPTC)系统,针对不考虑开关次数控制和考虑开关次数控制... 【目的】针对模型预测控制权重系数设计困难的问题,本文采用非支配排序遗传算法II(NSGA-II)和贝叶斯优化算法进行权重系数设计。【方法】基于永磁同步电机(PMSM)模型预测转矩控制(MPTC)系统,针对不考虑开关次数控制和考虑开关次数控制两种场景,分别采用NSGA-II和贝叶斯优化算法设计权重系数。不考虑开关次数控制时仅需设计一个权重系数,考虑开关次数控制时需同时设计两个权重系数。基于两种优化算法设计的权重系数,从控制效果、执行时间和内存占用对两种算法进行了对比。【结果】结果表明,对于考虑和不考虑开关次数控制的PMSM MPTC系统,两种权重系数设计算法均可行。NSGA-II得到的使适应度函数值最小的权重系数与贝叶斯优化算法得到的最优权重系数基本相当,控制性能也基本相当,贝叶斯优化算法的控制性能相对略优。【结论】NSGA-II可提供一组适合不同应用场景的Pareto最优解,但其算法复杂度高、计算时间长且占用内存大,适用于动态变化的运行场景。贝叶斯优化算法易于实现、占用资源少,在多控制目标的复杂场景中具有更好的寻优效果和更高的寻优效率。 展开更多
关键词 永磁同步电机 模型预测转矩控制 权重系数 开关次数控制 非支配排序遗传算法II 贝叶斯优化
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MAV-UAV combat organization's force formation plan generation based on NSGA-Ⅲ 认领 引用
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作者 ZHONG Yun WAN Lujun ZHANG Jieyong 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第1期307-317,共11页
Manned aerial vehicle-unmanned aerial vehicle(MAV-UAV)combat organization is a MAV-UAV combat collective formed from the perspective of organization design theory and methodology,and the generation of force formation ... Manned aerial vehicle-unmanned aerial vehicle(MAV-UAV)combat organization is a MAV-UAV combat collective formed from the perspective of organization design theory and methodology,and the generation of force formation plan is a key step in the organizational planning.Based on the description of the problem and the definition of organizational elements,the matching model of platform-target attack wave is constructed to minimize the redundancy of command and decision-making capability,resource capability and the number of platforms used.Based on the non-dominated sorting genetic algorithmⅢ(NSGA-Ⅲ)framework,which includes encoding/decoding method and constraint handling method,the generation model of organizational force formation plan is solved,and the effectiveness and superiority of the algorithm are verified by simulation experiments. 展开更多
关键词 manned-unmanned aerial vehicle combat organization force formation plan command and decision-making capability resource capability non-dominated sorting genetic algorithmⅢ(NSGA-Ⅲ)
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基于改进NSGA-Ⅱ的森林草原消防站多目标选址优化 认领 引用
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作者 李华 陈鑫 +1 位作者 益朋 吴立舟 《中国安全科学学报》 EI CAS CSCD 北大核心 2026年第3期171-177,共7页
为提升灭火救援队伍的应急响应能力与森林草原火灾防控布局的整体效能,提出基于混合防火应急道路的森林草原消防站选址优化方法。通过八向倾点算法结合数字高程模型(DEM),构建混合防火应急道路网络,提高消防队伍前期预防与应急响应能力... 为提升灭火救援队伍的应急响应能力与森林草原火灾防控布局的整体效能,提出基于混合防火应急道路的森林草原消防站选址优化方法。通过八向倾点算法结合数字高程模型(DEM),构建混合防火应急道路网络,提高消防队伍前期预防与应急响应能力;采用改进非支配排序遗传算法Ⅱ(NSGA-Ⅱ)的位置分配模型优化消防站选址,确保资源合理配置并提升覆盖范围。结果表明:混合防火应急道路对整体区域覆盖率为96.91%,对高风险区域覆盖率为93.51%,优化结果有助于提高救援队伍应对复杂地形的能力。优化后的消防站布局变异系数为0.26,能够保障消防队伍巡查与响应的能力。整体需求满意度为0.86,可确保关键区域得到充分保护。 展开更多
关键词 非支配排序遗传算法(NSGA-Ⅱ) 森林草原 消防站 多目标 选址优化 位置分配
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动态对抗场景中的打击链在线重构方法 认领 引用
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作者 薛羽阳 王涛 +3 位作者 廖馨 许元男 林鸣昊 吴安琪 《系统工程与电子技术》 EI CSCD 北大核心 2026年第6期1980-1990,共11页
针对动态对抗场景中因节点损毁导致打击链中断的问题,提出一种基于断点复用与局部搜索的在线重构方法。构建“物理-功能-状态”三层抽象网络模型,实现节点、连边与语义约束的统一建模。引入改进的非支配排序遗传算法(non-dominated sort... 针对动态对抗场景中因节点损毁导致打击链中断的问题,提出一种基于断点复用与局部搜索的在线重构方法。构建“物理-功能-状态”三层抽象网络模型,实现节点、连边与语义约束的统一建模。引入改进的非支配排序遗传算法(non-dominated sorting genetic algorithm,NSGA)-II以保留可用前段链路、压缩搜索空间,并结合逼近理想解的排序法(technique for order preference by similarity to ideal solution,TOPSIS)实现毫秒级的帕累托最优解决策。仿真结果表明,所提方法在链路重构时延上较传统策略降低62%,在装备资源下降20%的条件下仍可保持极高的任务完成率,且算法复杂度随节点规模线性增长,满足主流分布式作战体系对实时性与扩展性的需求。 展开更多
关键词 动态对抗 打击链 在线重构 多目标优化 非支配排序遗传算法 逼近理想解的排序法
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响应面法-NSGA-Ⅱ算法双目标优化金银花罗汉果茶泡制工艺 认领 引用
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作者 薛娟萍 孟宪福 +1 位作者 杨绍英 姚勇志 《食品安全质量检测学报》 CAS 2026年第14期256-264,共9页
目的对金银花罗汉果茶进行感官和绿原酸含量的双目标优化,获得最优工艺。方法采用单因素实验、Box-Behnken响应面设计对金银花提取工艺进行实验,再用响应面法和带精英策略的非支配排序遗传(non-dominated sorting genetic algorithmⅡ,N... 目的对金银花罗汉果茶进行感官和绿原酸含量的双目标优化,获得最优工艺。方法采用单因素实验、Box-Behnken响应面设计对金银花提取工艺进行实验,再用响应面法和带精英策略的非支配排序遗传(non-dominated sorting genetic algorithmⅡ,NSGA-Ⅱ)算法对金银花罗汉果茶进行感官和绿原酸含量的双目标优化,构建预测模型Y1和预测模型Y2,并对最优条件下制作的茶汤中的绿原酸含量、总酚含量和1,1-二苯基-2-三硝基苯肼(1,1-diphenyl-2-picrylhydrazyl,DPPH)自由基清除率进行检测。结果最优工艺为罗汉果添加量1.3 g/500 mL,泡制时间22.3 min、泡制温度88.1℃、金银花添加量2.3 g/500 mL,所得金银花罗汉果茶的感官评分为9.4分,其绿原酸含量为162.6μg/g。模型预测结果:金银花罗汉果茶的感官评分为9.72分,其绿原酸含量为159.25μg/g,模型误差均在5%以内,验证了模型的预测有效性。最优条件下制作的茶汤中绿原酸含量在前3 d降低较缓,超过第3 d后降低较快,总酚及自由基清除率随着贮存时间的延长呈现先增加后降低的趋势。结论本研究实现了活性成分含量与感官评价的同步提升,为金银花罗汉果茶工艺优化提供了系统化方法。 展开更多
关键词 响应面法 带精英策略的非支配排序遗传算法 双目标优化 金银花罗汉果茶
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改进VMD和TLS-N4SID的双馈风电机组次同步振荡参数辨识 认领 引用
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作者 郭国先 刘颖明 +3 位作者 王晓东 王瀚博 王若瑾 尚文祥 《电机与控制学报》 EI CSCD 北大核心 2026年第2期87-100,共14页
为了提高双馈感应发电机(DFIG)次同步振荡(SSO)参数辨识精度和噪声适应性以及消除辨识中存在的模态混叠,提出一种改进变分模态分解(VMD)和最小二乘-子空间状态空间系统(TLS-N4SID)的DFIG的SSO参数辨识方法。基于VMD分解DFIG并网电流,并... 为了提高双馈感应发电机(DFIG)次同步振荡(SSO)参数辨识精度和噪声适应性以及消除辨识中存在的模态混叠,提出一种改进变分模态分解(VMD)和最小二乘-子空间状态空间系统(TLS-N4SID)的DFIG的SSO参数辨识方法。基于VMD分解DFIG并网电流,并采用贝叶斯优化算法(BO)对VMD进行改进,获得最优本征模态函数(IMF)分解个数K和惩罚因子α,以消除分解中的模态混叠现象和提高噪声适应性。将得到的IMFs与并网电流进行互信息(MI)分析,选取出主导IMFs。重新采样主导IMFs并基于TLS-N4SID进行参数辨识,辨识过程中采用非支配排序遗传算法II(NSGA-II)对N4SID进行改进,获得最优信号子空间阶数b,以提高辨识精准性和噪声适应性,再结合TLS完成DFIG的SSO信号的参数辨识。通过复合信号、含双馈风电场的4机2区域的系统模型的时域仿真以及河北沽源风电场实际SSO数据进行分析,验证所提出辨识方法的有效性。 展开更多
关键词 双馈感应发电机 次同步振荡 贝叶斯优化 变分模态分解 非支配排序遗传算法Ⅱ 最小二乘-子空间状态空间系统 参数辨识
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基于NSGA-Ⅱ算法与离散模块梁单元水弹性方法的连接件优化设计分析 认领 引用
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作者 陈永强 张显涛 《中国舰船研究》 CSCD 北大核心 2026年第3期158-167,共10页
[目的]在离散模块梁单元(DMB)框架下,针对浮箱型多模块海上漂浮式光伏(OFPV)平台连接件刚度优化问题,提出一种新的方法。[方法]首先,介绍DMB水弹性分析方法,给出连接件刚度矩阵的形式并简述水弹性响应的数值建模方法;其次,给出线性加权... [目的]在离散模块梁单元(DMB)框架下,针对浮箱型多模块海上漂浮式光伏(OFPV)平台连接件刚度优化问题,提出一种新的方法。[方法]首先,介绍DMB水弹性分析方法,给出连接件刚度矩阵的形式并简述水弹性响应的数值建模方法;其次,给出线性加权遗传算法和非支配排序遗传算法Ⅱ(NSGA-Ⅱ)的求解步骤,重点介绍刚度编码的实数码、指数码和科学记数码这3种基因编码方式以及其对应的交叉和变异算子,并进行对比分析;最后,引入等效零刚度和等效无穷刚度缩聚解空间。[结果]结果显示,使用NSGA-Ⅱ算法可求解得出最大结构剪力最小和最大结构弯矩最小的Pareto前沿,同时,该Pareto前沿可视作由线性加权法得到的不同权重设置所对应最优解的集合,且科学记数码的搜索性能优。[结论]所述优化理论模型可针对浮箱型多模块平台用于对连接件刚度进行多目标优化。 展开更多
关键词 连接件 刚度 离散模块梁单元 非支配排序遗传算法Ⅱ 多目标优化 科学记数码
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Dual-Objective Mixed Integer Linear Program and Memetic Algorithm for an Industrial Group Scheduling Problem 认领 引用 被引量：13
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作者 Ziyan Zhao Shixin Liu +1 位作者 MengChu Zhou Abdullah Abusorrah 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2021年第6期1199-1209,共11页
Group scheduling problems have attracted much attention owing to their many practical applications.This work proposes a new bi-objective serial-batch group scheduling problem considering the constraints of sequence-de... Group scheduling problems have attracted much attention owing to their many practical applications.This work proposes a new bi-objective serial-batch group scheduling problem considering the constraints of sequence-dependent setup time,release time,and due time.It is originated from an important industrial process,i.e.,wire rod and bar rolling process in steel production systems.Two objective functions,i.e.,the number of late jobs and total setup time,are minimized.A mixed integer linear program is established to describe the problem.To obtain its Pareto solutions,we present a memetic algorithm that integrates a population-based nondominated sorting genetic algorithm II and two single-solution-based improvement methods,i.e.,an insertion-based local search and an iterated greedy algorithm.The computational results on extensive industrial data with the scale of a one-week schedule show that the proposed algorithm has great performance in solving the concerned problem and outperforms its peers.Its high accuracy and efficiency imply its great potential to be applied to solve industrial-size group scheduling problems. 展开更多
关键词 Insertion-based local search iterated greedy algorithm machine learning memetic algorithm nondominated sorting genetic algorithm II(NSGA-II) production scheduling
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燃气调节阀低扭矩优化设计及试验研究 认领 引用
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作者 刘广奥 陈英龙 +2 位作者 罗畅敏 闫博 高飞 《工程设计学报》 北大核心 2026年第1期117-129,146,共13页
针对燃气调节阀启闭过程中的高扭矩问题,开展多因素分析与结构优化设计研究,提出了结合拓扑优化、响应面法与非支配排序遗传算法II的低扭矩优化方法。通过建立调节阀启闭扭矩理论模型,明确了机械摩擦扭矩为主要影响因素,并重点分析了介... 针对燃气调节阀启闭过程中的高扭矩问题,开展多因素分析与结构优化设计研究,提出了结合拓扑优化、响应面法与非支配排序遗传算法II的低扭矩优化方法。通过建立调节阀启闭扭矩理论模型,明确了机械摩擦扭矩为主要影响因素,并重点分析了介质作用力、弹簧预紧力和格莱圈压缩率对扭矩与密封性能的耦合效应。在结构优化中,通过拓扑优化对阀座形态进行了重构,以减小有效的介质作用面积,降低摩擦阻力;随后,基于响应面回归模型构建了以机械摩擦扭矩和泄漏量为目标的多目标优化模型,并结合非支配排序遗传算法II实现了扭矩与密封性能的协同优化。试验结果表明:在5.2 MPa介质压力下,优化后调节阀的机械摩擦扭矩降低了71.8%,验证了所提出优化方法的准确性与可行性。研究结果为燃气调节阀的高性能设计与国产化奠定了理论基础。 展开更多
关键词 燃气调节阀 低扭矩 拓扑优化 响应面法 非支配排序遗传算法II
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