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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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Multi-objective optimization of water supply network rehabilitation with non-dominated sorting Genetic Algorithm-II 认领 引用 被引量：4
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作者 Xi JIN Jie ZHANG +1 位作者 Jin-liang GAO Wen-yan WU 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS 2008年第3期391-400,共10页
Through the transformation of hydraulic constraints into the objective functions associated with a water supply network rehabilitation problem, a non-dominated sorting Genetic Algorithm-II (NSGA-II) can be used to sol... Through the transformation of hydraulic constraints into the objective functions associated with a water supply network rehabilitation problem, a non-dominated sorting Genetic Algorithm-II (NSGA-II) can be used to solve the altered multi-objective optimization model. The introduction of NSGA-II into water supply network optimal rehabilitation problem solves the conflict between one fitness value of standard genetic algorithm (SGA) and multi-objectives of rehabilitation problem. And the uncertainties brought by using weight coefficients or punish functions in conventional methods are controlled. And also by in-troduction of artificial inducement mutation (AIM) operation, the convergence speed of population is accelerated;this operation not only improves the convergence speed, but also improves the rationality and feasibility of solutions. 展开更多
关键词 Water supply system Water supply network Optimal rehabilitation Multi-objective Non-dominated sorting Ge-netic Algorithm （NSGA）
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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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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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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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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
GA-SVM结合NSGA-Ⅲ对开关磁阻电机多目标优化设计 认领 引用
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作者 周程涛 陈刚 +1 位作者 邓琪 柏恋凡 《湖南工业大学学报》 2026年第3期17-23,共7页
针对开关磁阻电机驱动性能脉动大、效率低的问题,提出了一种基于支持向量机优化的预测模型(GA-SVM)与第三代非支配遗传算法(NSGA-Ⅲ)相结合的多目标优化策略。仿真结果表明,该方法对开关磁阻电机的平均转矩和效率有较大提高,同时降低了... 针对开关磁阻电机驱动性能脉动大、效率低的问题,提出了一种基于支持向量机优化的预测模型(GA-SVM)与第三代非支配遗传算法(NSGA-Ⅲ)相结合的多目标优化策略。仿真结果表明,该方法对开关磁阻电机的平均转矩和效率有较大提高,同时降低了转矩脉动。通过建立一个开关磁阻电机仿真模型,并运用灵敏度分析选取影响因数高的参数作为决策变量,运用超拉丁方采样对开关磁阻电机进行数据采样,以有限元法计算出响应值、GA-SVM和NSGA-Ⅲ算法相结合对电机进行多目标寻优,优化后的数据加入权重系数权衡后得到最优解,仿真结果验证了所提方法的有效性。 展开更多
关键词 开关磁阻电机 灵敏度分析 支持向量机 多目标寻优 第三代非支配排序遗传算法
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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：2
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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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基于改进NSGA-Ⅱ算法的航空器滑行路径多目标优化 认领 引用 被引量：2
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作者 钟庆伟 唐浩铭 +3 位作者 庾映雪 张永祥 姚俊杰 潘明思语 《科学技术与工程》 EI 北大核心 2025年第20期8737-8744,共8页
随着全球航空业的快速发展,机场场面航空器滑行管理难度增加,如何在保障安全和提升效率的同时减少对环境的影响变得尤为重要。针对该问题,以预防滑行路径冲突为基础约束条件,以滑行时间最短和二氧化碳(carbon dioxide,CO2)排放量最... 随着全球航空业的快速发展,机场场面航空器滑行管理难度增加,如何在保障安全和提升效率的同时减少对环境的影响变得尤为重要。针对该问题,以预防滑行路径冲突为基础约束条件,以滑行时间最短和二氧化碳(carbon dioxide,CO2)排放量最小为优化目标建立混合整数线性优化模型,并设计非支配排序遗传算法Ⅱ(non-dominated sorting genetic algorithmⅡ,NSGA-Ⅱ)进行动态求解。最后,以中国某枢纽机场为算例背景,借助Python语言实现NSGA-Ⅱ算法,并与商业优化求解器Gurobi进行对比。计算结果表明:航空器数量为14架次时,与优化前相比,总滑行时间减少约17.46%,CO2排放量降低约18.35%;NSGA-Ⅱ算法得到的可行解与Gurobi所求最优解间的距离为1.083%,但NSGA-Ⅱ的求解时间相对减少95.0%。同时,通过多个算例测试表明,NSGA-Ⅱ算法在处理大规模多目标路径优化问题时具有显著优势。所提出的优化方案可有效提升机场场面运营效率并减少CO2排放。 展开更多
关键词 滑行路径优化 多目标优化 非支配排序遗传算法(NSGA-Ⅱ) 数学求解器 动态优化 CO2排放
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Strengthened Dominance Relation NSGA-Ⅲ Algorithm Based on Differential Evolution to Solve Job Shop Scheduling Problem 认领 引用 被引量：5
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作者 Liang Zeng Junyang Shi +2 位作者 Yanyan Li Shanshan Wang Weigang Li 《Computers, Materials & Continua》 SCIE EI 2024年第1期375-392,共18页
The job shop scheduling problem is a classical combinatorial optimization challenge frequently encountered in manufacturing systems.It involves determining the optimal execution sequences for a set of jobs on various ... The job shop scheduling problem is a classical combinatorial optimization challenge frequently encountered in manufacturing systems.It involves determining the optimal execution sequences for a set of jobs on various machines to maximize production efficiency and meet multiple objectives.The Non-dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ)is an effective approach for solving the multi-objective job shop scheduling problem.Nevertheless,it has some limitations in solving scheduling problems,including inadequate global search capability,susceptibility to premature convergence,and challenges in balancing convergence and diversity.To enhance its performance,this paper introduces a strengthened dominance relation NSGA-Ⅲ algorithm based on differential evolution(NSGA-Ⅲ-SD).By incorporating constrained differential evolution and simulated binary crossover genetic operators,this algorithm effectively improves NSGA-Ⅲ’s global search capability while mitigating pre-mature convergence issues.Furthermore,it introduces a reinforced dominance relation to address the trade-off between convergence and diversity in NSGA-Ⅲ.Additionally,effective encoding and decoding methods for discrete job shop scheduling are proposed,which can improve the overall performance of the algorithm without complex computation.To validate the algorithm’s effectiveness,NSGA-Ⅲ-SD is extensively compared with other advanced multi-objective optimization algorithms using 20 job shop scheduling test instances.The experimental results demonstrate that NSGA-Ⅲ-SD achieves better solution quality and diversity,proving its effectiveness in solving the multi-objective job shop scheduling problem. 展开更多
关键词 Multi-objective job shop scheduling non-dominated sorting genetic algorithm differential evolution simulated binary crossover
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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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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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基于NSGA-Ⅱ算法的柔性气缸弹射影响参数优化研究 认领 引用 被引量：1
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作者 王卓越 杨宝生 +2 位作者 姜毅 杨哩娜 王汉平 《振动与冲击》 EI CSCD 北大核心 2025年第9期99-108,共10页
柔性气缸弹射作为一种新型弹射方法,具有红外目标隐蔽,能量输出稳定等优点。为解决柔性气缸弹射过载较大、响应时间较长的问题,进一步提高弹射响应速度和弹射稳定性,引入了一种代理模型优化方法对柔性气缸弹射过程进行优化,旨在减小弹... 柔性气缸弹射作为一种新型弹射方法,具有红外目标隐蔽,能量输出稳定等优点。为解决柔性气缸弹射过载较大、响应时间较长的问题,进一步提高弹射响应速度和弹射稳定性,引入了一种代理模型优化方法对柔性气缸弹射过程进行优化,旨在减小弹射过载并提升弹射速度。基于代理模型理论,建立柔性气缸弹射代理模型,对代理模型进行精度分析,在此基础上,深入探究了充气孔直径、开启时间以及开启时长这三个关键参数对弹射动力学响应的具体影响。结合NSGA-Ⅱ(non-dominated sorting genetic algorithm II)优化算法,对弹射模型的相关参数进行了优化处理。研究结果显示:采用粒子法的有限元模型能够精确模拟柔性气缸的弹射过程;进一步的分析表明,相较于响应面模型Kriging代理模型在替代柔性气缸有限元模型方面展现出了更高的准确性。针对初始设计点,提出了通过NSGA-Ⅱ算法优化的均衡设计方案,该方案成功地将弹射速度提升了4.79%,同时将弹射过载降低了21.70%;并针对弹射速度与最大过载的优化过程给出了优化方案。 展开更多
关键词 粒子法 柔性气缸弹射 Kriging代理模型 NSGA-Ⅱ算法
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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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