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GREEDY NON-DOMINATED SORTING IN GENETIC ALGORITHM-ⅡFOR VEHICLE ROUTING PROBLEM IN DISTRIBUTION 认领 引用 被引量：4
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作者 WEI Tian FAN Wenhui XU Huayu 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS 2008年第6期18-24,共7页
Vehicle routing problem in distribution(VRPD)is a widely used type of vehicle routing problem(VRP),which has been proved as NP-Hard,and it is usually modeled as single objective optimization problem when modeling.For ... Vehicle routing problem in distribution(VRPD)is a widely used type of vehicle routing problem(VRP),which has been proved as NP-Hard,and it is usually modeled as single objective optimization problem when modeling.For multi-objective optimization model,most researches consider two objectives.A multi-objective mathematical model for VRP is proposed,which considers the number of vehicles used,the length of route and the time arrived at each client.Genetic algorithm is one of the most widely used algorithms to solve VRP.As a type of genetic algorithm(GA),non-dominated sorting in genetic algorithm-Ⅱ(NSGA-Ⅱ)also suffers from premature convergence and enclosure competition.In order to avoid these kinds of shortage,a greedy NSGA-Ⅱ(GNSGA-Ⅱ)is proposed for VRP problem.Greedy algorithm is implemented in generating the initial population,cross-over and mutation.All these procedures ensure that NSGA-Ⅱis prevented from premature convergence and refine the performance of NSGA-Ⅱat each step.In the distribution problem of a distribution center in Michigan,US,the GNSGA-Ⅱis compared with NSGA-Ⅱ.As a result,the GNSGA-Ⅱis the most efficient one and can get the most optimized solution to VRP problem.Also,in GNSGA-Ⅱ,premature convergence is better avoided and search efficiency has been improved sharply. 展开更多
关键词 Greedy non-dominated sorting in genetic algorithm-Ⅱ(GNSGA-Ⅱ) Vehicle routing problem(VRP) Multi-objective optimization
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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
Improved non-dominated sorting genetic algorithm (NSGA)-II in multi-objective optimization studies of wind turbine blades 认领 引用 被引量：34
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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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Optimization of dynamic aperture by using non-dominated sorting genetic algorithm-Ⅱ in a diffraction-limited storage ring with solenoids for generating round beam 认领 引用 被引量：1
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作者 Chongchong Du Sheng Wang +2 位作者 Jiuqing Wang Saike Tian Jinyu Wan 《Radiation Detection Technology and Methods》 CSCD 2023年第2期271-278,共8页
Purpose Round beam,i.e.,with equal horizontal and vertical emittance,is preferable than a horizontally flat one for some beamline applications in Diffraction-limited storage rings(DLSRs),for the purposes of reducing t... Purpose Round beam,i.e.,with equal horizontal and vertical emittance,is preferable than a horizontally flat one for some beamline applications in Diffraction-limited storage rings(DLSRs),for the purposes of reducing the number of photons getting discarded and better phase space match between photon and electron beam.Conventional methods of obtaining round beam inescapably results in a reduction of dynamic aperture(DA).In order to recover the DA as much as possible for improving the injection efficiency,the DA optimization by using Non-dominated sorting genetic algorithm-Ⅱ(NSGA-Ⅱ)to generate round beam,particularly to one of the designed lattice of the High Energy Photon Source(HEPS)storage ring,are presented.Method According to the general unconstrained model of NSGA-Ⅱ,we modified the standard model by using parallel computing to optimize round beam lattices with errors,especially for a strong coupling,such as solenoid scheme.Results and conclusion The results of numerical tracking verify the correction of the theory framework of solenoids with fringe fields and demonstrates the feasibility on the HEPS storage ring with errors to operate in round beam mode after optimizing DA. 展开更多
关键词 Diffraction-limited storage rings Round beam Non-dominated sorting genetic Algorithm-Ⅱ High energy photon source
基于DNN-NSGA-Ⅱ的高填方加筋边坡参数优化研究 认领 引用 被引量：1
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作者 查文华 谭雪剑 +3 位作者 许涛 徐源歆 赖斯祾 纪超 《水力发电》 CAS 2026年第1期45-51,共7页
以福建某典型高填方加筋边坡为研究对象,提出一种集成深度神经网络(DNN)与非支配排序遗传算法(NSGA-Ⅱ)的智能化优化设计方法,用于实现高填方加筋边坡支护设计的多目标协同优化。首先,通过有限元模拟生成样本数据,构建以关键设计参数为... 以福建某典型高填方加筋边坡为研究对象,提出一种集成深度神经网络(DNN)与非支配排序遗传算法(NSGA-Ⅱ)的智能化优化设计方法,用于实现高填方加筋边坡支护设计的多目标协同优化。首先,通过有限元模拟生成样本数据,构建以关键设计参数为输入、稳定性响应指标为输出的DNN代理模型;随后,将该代理模型嵌入NSGA-Ⅱ框架,实现以最小化水平位移、加筋材料用量与最大化安全系数为目标的多目标寻优。通过对Pareto前沿解集的分析与典型方案提取,验证所提方法在兼顾边坡安全性与经济性方面的有效性,可为高填方边坡优化设计提供理论支撑与工程参考。 展开更多
关键词 高填方边坡 加筋设计 多目标优化 深度神经网络 非支配排序遗传算法
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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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响应面与NSGA-Ⅱ协同折弯机多目标优化方法研究 认领 引用
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作者 陈广庆 周鹏 +2 位作者 张兵 陈玉伦 陈彦华 《现代制造工程》 北大核心 2026年第7期135-141,94,共7页
针对某100 t液压钣金折弯机结构优化问题,提出了一种响应面与NSGA-Ⅱ协同折弯机多目标优化设计方法。通过构建包括折弯机机架宽度、肋板宽度与滑台宽度的参数化模型分析关键设计变量,利用中心复合设计(Central Composite Design,CCD)方... 针对某100 t液压钣金折弯机结构优化问题,提出了一种响应面与NSGA-Ⅱ协同折弯机多目标优化设计方法。通过构建包括折弯机机架宽度、肋板宽度与滑台宽度的参数化模型分析关键设计变量,利用中心复合设计(Central Composite Design,CCD)方法获得15组实验样本,系统分析了设计变量对折弯机质量、变形量及应力的非线性耦合关系,最后利用NSGA-Ⅱ算法对折弯机质量、变形量及应力进行多目标优化。优化后折弯机质量减少了7.1%,最大变形量减少了20.0%,提升了轻量化水平与结构刚度,最大应力虽然增加了50%,但仍低于Q235钢屈服强度。研究结果表明,所提方法实现了对折弯机的轻量化、刚度提升与强度约束之间的有效平衡,验证了响应面法在复杂装备多目标优化中的工程适用性,为高精度钣金加工设备设计提供了理论依据与技术路径。 展开更多
关键词 折弯机 结构优化 响应面法 轻量化 非支配排序遗传算法Ⅱ
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基于NSGA-Ⅱ算法的环形三角管桁架施工分段智能生成方法 认领 引用
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作者 芦燕 芦睿 +3 位作者 齐朋 鲁建 高杨 杨乐 《天津大学学报（自然科学与工程技术版）》 EI CAS CSCD 北大核心 2026年第8期815-826,共12页
本研究将非支配排序遗传算法Ⅱ(NSGA-Ⅱ)多目标优化算法应用于环形三角管桁架的分段施工过程,实现了施工成本、工期与结构安全性的多目标优化.简化了三角形管桁架的分段的表示方法,提出针对三角管桁架的基本单元划分方法,并提出基本单... 本研究将非支配排序遗传算法Ⅱ(NSGA-Ⅱ)多目标优化算法应用于环形三角管桁架的分段施工过程,实现了施工成本、工期与结构安全性的多目标优化.简化了三角形管桁架的分段的表示方法,提出针对三角管桁架的基本单元划分方法,并提出基本单元矩阵的概念,将物理模型转化为数学模型,将整体桁架结构的分段问题转化为基本单元的排列组合问题,通过生成不同的数组并对其进行排列,实现了不同分段方式的生成;建立了成本-工期-安全性多目标优化模型,实现针对起重机械的优选及随分段情况变化的胎架布设成本的计算,确定了以内环桁架吊装时间为关键线路的工期优化模型,确定了以构形度作为结构安全性的安全性优化模型;通过对比NSGA-Ⅱ算法优化的结果与案例工程的分段结果可得,算法计算出的结果在成本、工期、安全性上均存在一定程度的优化,可以验证算法的可行性;通过使用熵权法赋权、层次分析法修正的方法,确定了成本、工期和安全性的权重分别为0.0438、0.4247和0.5315.最终,分段方案1的相对贴近度最高,在成本、工期和安全性系数上较原方案分别优化了5.2%、24.0%和116.0%. 展开更多
关键词 多目标优化 非支配排序遗传算法Ⅱ 三角管桁架 分段吊装
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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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基于NSGA-Ⅱ的船厂自动化立体仓库共轨式双堆垛机多目标优化调度方法 认领 引用
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作者 王昊 刘巧媚 +2 位作者 周磊 李敬花 宋得宁 《造船技术》 2026年第2期21-25,44,共5页
针对船厂自动化立体仓库(Automated Storage and Retrieval System,AS/RS)单堆垛机的任务分配不均衡和能耗高等问题,提出一种基于改进非劣分层遗传算法(Non-dominated Sorting Genetic AlgorithmⅡ,NSGA-Ⅱ)的共轨式双堆垛机多目标优化... 针对船厂自动化立体仓库(Automated Storage and Retrieval System,AS/RS)单堆垛机的任务分配不均衡和能耗高等问题,提出一种基于改进非劣分层遗传算法(Non-dominated Sorting Genetic AlgorithmⅡ,NSGA-Ⅱ)的共轨式双堆垛机多目标优化调度方法。从问题描述、数学模型目标函数选取、数学模型约束条件设定和编码设计等方面进行双堆垛机多目标优化调度分析,确定NSGA-Ⅱ流程,并进行试验验证。结果表明,该方法可为船厂AS/RS自动化仓储调度提供高效的算法解决方案,对提升仓储系统物流效率具有实际应用价值。 展开更多
关键词 船厂 自动化立体仓库 共轨式双堆垛机 多目标优化调度 改进非劣分层遗传算法 遗传算法 粒子群优化
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Models for Location Inventory Routing Problem of Cold Chain Logistics with NSGA-Ⅱ Algorithm 认领 引用 被引量：3
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作者 郑建国 李康 伍大清 《Journal of Donghua University(English Edition)》 CAS 2017年第4期533-539,共7页
In this paper,a novel location inventory routing(LIR)model is proposed to solve cold chain logistics network problem under uncertain demand environment. The goal of the developed model is to optimize costs of location... In this paper,a novel location inventory routing(LIR)model is proposed to solve cold chain logistics network problem under uncertain demand environment. The goal of the developed model is to optimize costs of location,inventory and transportation.Due to the complex of LIR problem( LIRP), a multi-objective genetic algorithm(GA), non-dominated sorting in genetic algorithm Ⅱ( NSGA-Ⅱ) has been introduced. Its performance is tested over a real case for the proposed problems. Results indicate that NSGA-Ⅱ provides a competitive performance than GA,which demonstrates that the proposed model and multi-objective GA are considerably efficient to solve the problem. 展开更多
关键词 cold chain logistics multi-objective location inventory routing problem(LIRP) non-dominated sorting in genetic algorithm Ⅱ(NSGA-Ⅱ)
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“双碳”目标下基于改进型NSGA-Ⅱ的港口作业调度优化算法 认领 引用 被引量：3
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作者 刘树东 吴昊 +1 位作者 丛佳 顾播宇 《计算机应用》 CSCD 北大核心 2025年第6期1945-1953,共9页
随着全球气候变化问题的日益严峻,我国提出了“双碳”目标(碳达峰和碳中和)。而港口作为物流枢纽和货物集散地,它的碳排放问题尤为突出。针对港口作业调度优化问题,考虑船舶到港时间、货物装卸需求、岸桥作业能力及碳排放成本等关键因素... 随着全球气候变化问题的日益严峻,我国提出了“双碳”目标(碳达峰和碳中和)。而港口作为物流枢纽和货物集散地,它的碳排放问题尤为突出。针对港口作业调度优化问题,考虑船舶到港时间、货物装卸需求、岸桥作业能力及碳排放成本等关键因素,构建最小化碳排放成本和码头运营成本的作业调度优化模型,并提出一种“双碳”目标下基于改进型非支配排序遗传算法(NSGA-Ⅱ)(E-NSGA-Ⅱ)的港口作业调度优化算法。首先,调整算法的编码策略、种群初始化方法和交叉变异操作;其次,设计不可行解的基因修复算子,并引入自适应交叉与变异概率机制。实验结果表明,与FCFS(First Come First Service)调度算法相比,所提算法在模型求解中的总成本下降了7.9%,碳排放成本下降了19.7%,码头运营成本下降了6.5%。以上研究结果丰富了多目标优化算法和港口作业调度理论,并为港口企业实现绿色调度、降低运营成本和提升经济效益提供了有力支持。 展开更多
关键词 “双碳”目标 碳排放 码头运营成本 港口作业调度优化算法 NSGA-Ⅱ
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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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Optimising PID Controllers for Multi-Area Automatic Generation Control With Improved NSGA-Ⅱ 认领 引用
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作者 Yang Yang Yuchao Gao +1 位作者 Shangce Gao Jinran Wu 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2025年第4期1135-1147,共13页
Modern automated generation control(AGC)is increasingly complex,requiring precise frequency control for stability and operational accuracy.Traditional PID controller optimisation methods often struggle to handle nonli... Modern automated generation control(AGC)is increasingly complex,requiring precise frequency control for stability and operational accuracy.Traditional PID controller optimisation methods often struggle to handle nonlinearities and meet robustness requirements across diverse operational scenarios.This paper introduces an enhanced strategy using a multi-objective optimisation framework and a modified non-dominated sorting genetic algorithm Ⅱ(SNSGA).The proposed model optimises the PID controller by minimising key performance metrics:integration time squared error(ITSE),integration time absolute error(ITAE),and rate of change of deviation(J).This approach balances convergence rate,overshoot,and oscillation dynamics effectively.A fuzzy-based method is employed to select the most suitable solution from the Pareto set.The comparative analysis demonstrates that the SNSGA-based approach offers superior tuning capabilities over traditional NSGA-Ⅱ and other advanced control methods.In a two-area thermal power system without reheat,the SNSGA significantly reduces settling times for frequency deviations:2.94s for Δf1 and 4.98s for Δf2,marking improvements of 31.6%and 13.4%over NSGA-Ⅱ,respectively. 展开更多
关键词 automatic generation control load frequency control multi-objective optimization nondominated sorting genetic algorithmⅡ PID controller
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Optimization of solar thermal power station LCOE based on NSGA-Ⅱ algorithm 认领 引用 被引量：3
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作者 LI Xin-yang LU Xiao-juan DONG Hai-ying 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2018年第1期1-8,共8页
In view of the high cost of solar thermal power generation in China,it is difficult to realize large-scale production in engineering and industrialization.Non-dominated sorting genetic algorithm II(NSGA-II)is applied ... In view of the high cost of solar thermal power generation in China,it is difficult to realize large-scale production in engineering and industrialization.Non-dominated sorting genetic algorithm II(NSGA-II)is applied to optimize the levelling cost of energy(LCOE)of the solar thermal power generation system in this paper.Firstly,the capacity and generation cost of the solar thermal power generation system are modeled according to the data of several sets of solar thermal power stations which have been put into production abroad.Secondly,the NSGA-II genetic algorithm and particle swarm algorithm are applied to the optimization of the solar thermal power station LCOE respectively.Finally,for the linear Fresnel solar thermal power system,the simulation experiments are conducted to analyze the effects of different solar energy generation capacities,different heat transfer mediums and loan interest rates on the generation price.The results show that due to the existence of scale effect,the greater the capacity of the power station,the lower the cost of leveling and electricity,and the influence of the types of heat storage medium and the loan on the cost of leveling electricity are relatively high. 展开更多
关键词 solar thermal power generation levelling cost of energy(LCOE) linear Fresnel non-dominated sorting genetic algorithm II(NSGA-II)
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基于DQN的改进NSGA-Ⅱ求解多目标柔性作业车间调度问题 认领 引用 被引量：1
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作者 郑国梁 张朝阳 +1 位作者 吉卫喜 于俊杰 《现代制造工程》 北大核心 2025年第9期1-11,共11页
提出了一种基于深度Q网络(Deep Q-Network,DQN)改进的非支配排序遗传算法(Non-dominated Sorting Genetic AlgorithmⅡ,NSGA-Ⅱ),以解决以最小化最大完工时间和最小化能源消耗为目标的多目标柔性作业车间调度问题(Multi-Objective Flexi... 提出了一种基于深度Q网络(Deep Q-Network,DQN)改进的非支配排序遗传算法(Non-dominated Sorting Genetic AlgorithmⅡ,NSGA-Ⅱ),以解决以最小化最大完工时间和最小化能源消耗为目标的多目标柔性作业车间调度问题(Multi-Objective Flexible Job shop Scheduling Problem,MO-FJSP)。通过在DQN算法中定义马尔可夫决策过程和奖励函数,考虑选定设备对完工时间和能源消耗的局部及全局影响,提高了NSGA-Ⅱ初始种群的质量。改进的NSGA-Ⅱ通过精英保留策略确保运行过程中的种群多样性,并保留了进化过程中优质的个体。将DQN算法生成的初始解与贪婪算法生成的初始解进行对比,验证了DQN算法在生成初始解方面的有效性。此外,将基于DQN算法的改进NSGA-Ⅱ与其他启发式算法在标准案例和仿真案例上进行对比,证明了其在解决MO-FJSP方面的有效性。 展开更多
关键词 深度Q网络算法 多目标柔性作业车间调度问题 奖励函数 非支配排序遗传算法
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基于非支配排序遗传算法Ⅱ的10 kV配电线路故障自动识别技术 认领 引用 被引量：1
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作者 付丽伟 高维钊 +2 位作者 李名泰 刘轩豪 贾健宇 《电气技术》 2025年第11期64-69,共6页
配电线路故障识别仅依赖单一的电流波形,未能充分考虑故障的复杂性,对于全局故障点的识别时间较长。为此,本文提出基于非支配排序遗传算法Ⅱ(NSGA-Ⅱ)的10kV配电线路故障自动识别技术。首先,分析电流波形的差异,利用电流波形相关度比较... 配电线路故障识别仅依赖单一的电流波形,未能充分考虑故障的复杂性,对于全局故障点的识别时间较长。为此,本文提出基于非支配排序遗传算法Ⅱ(NSGA-Ⅱ)的10kV配电线路故障自动识别技术。首先,分析电流波形的差异,利用电流波形相关度比较的方法,初步定位10kV配电线路故障区段,再运用NSGA-Ⅱ对初步定位的故障区段进行迭代优化,输出初步定位故障区段最优解。然后,基于初步故障区段最优解,依据故障后电流、电压动态特性,计算启动电流等故障电气量,并对各区段故障电气量信息进行优化与整定。最后,在正常和故障状态下,根据整定值和电压、电流变化情况,利用奇异度评价方法自动识别故障点。仿真实验结果表明,所提方法能成功检测10kV配电线路故障,且在3s内完成所有故障点识别,具有显著优势。 展开更多
关键词 非支配排序遗传算法Ⅱ(NSGA-Ⅱ) 10kV配电线路 配电网故障识别 定值保护 奇异度评价
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OTPA结合NSGA-Ⅱ算法的产品包装系统优化设计 认领 引用
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作者 陆怡宇 张元标 +1 位作者 杨松平 聂楚昕 《振动与冲击》 EI CSCD 北大核心 2025年第1期102-112,共11页
利用工况传递路径分析(operational transfer path analysis,OTPA)方法分析随机振动不同激励谱型、不同振动等级下产品包装系统的振动传递特性,结合非支配排序遗传算法(non-dominated sorting genetic algorithm-Ⅱ,NSGA-Ⅱ)进行包装系... 利用工况传递路径分析(operational transfer path analysis,OTPA)方法分析随机振动不同激励谱型、不同振动等级下产品包装系统的振动传递特性,结合非支配排序遗传算法(non-dominated sorting genetic algorithm-Ⅱ,NSGA-Ⅱ)进行包装系统优化设计。试验结果表明:产品关键元件实测振动加速度响应曲线与OTPA方法合成的加速度响应曲线吻合良好,验证了OTPA方法的正确性;通过OTPA方法量化各传递路径的振动贡献量,对比识别出产品包装系统的主要振动传递路径;保持非主要传递路径的缓冲衬垫材料不变,应用NSGA-Ⅱ算法优化产品包装件系统中主要振动传递路径处的缓冲衬垫分配,有效降低了关键元件的加速度响应,减少在振动过程中的能量聚集,促使各传递路径的振动贡献量趋于均衡。实现了以缓冲性能为主导,同时兼顾环保性能与成本的包装系统优化设计,验证了优化方法的有效性,为产品包装系统设计提供参考。 展开更多
关键词 随机振动 工况传递路径分析(OTPA) 振动贡献量 非支配排序遗传算法(NSGA-Ⅱ) 减振优化
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