检索结果-内蒙古大学图书馆

A multi-objective optimization study on the electromechanical system for a space mechanism based on a Catboost surrogate model and nsga-iii algorithm

引用

EXPERT SYSTEMS WITH APPLICATIONS 2025年 268卷

作者： Zhang, Xiuhua Liu, Huaiju Hu, Mingzhu Zhang, Yangming Zhu, Caichao Chongqing Univ State Key Lab Mech Transmiss Adv Equipment Chongqing 400030 Peoples R China

The electromechanical system is a critical component of space mechanisms, tasks with roles such as the installation and maintenance of space station equipment, as well as docking and separation of spacecraft. There is a continuous demand for large torque, high efficiency, long life, and lightweight design for the electromechanical system of space mechanisms. To meet the design requirements of electromechanical system for a space mechanism, a multi-objective optimization model based on the Catboost surrogate approach and the nsga-iii algorithm is developed, which rapidly solves for 71 structural parameters. With the goals of lightweight, high reliability and high efficiency, a reduction of 3.80% in weight, an improvement of 10.71% in strength and an improvement of 6.71% in transmission efficiency are achieved. This study provides a design approach for the development of next-generation transmission system for space mechanisms.

关键词： Space mechanism Electromechanical system Surrogate model nsga-iii algorithm Performance optimization

来源：评论

学校读者我要写书评

暂无评论

An Improved nsga-iii algorithm for Scheduling Ships Arrival and Departure the Main Channel of Tianjin Port

引用

IEEE ACCESS 2024年 12卷 131442-131457页

作者： Ren, Luzhen Li, Yuzheng Zhou, Shibo Jimei Univ Nav Coll Xiamen 361021 Peoples R China

An effective scheduling method for ship arrival and departure is crucial for maximizing port resources and improving waterway navigation efficiency. In order to enhance the efficacy of ship arrival and departure scheduling, a scheduling model is developed with the objective of minimizing the total scheduling time and minimizing the total waiting time of the ship. Furthermore, an enhanced three-generation non-dominated sorting genetic algorithm, nsga-iii, is proposed as a solution to the ship scheduling model. The improved nsga-iii algorithm employs the Metropolis criterion in the simulated annealing algorithm in conjunction with the small habitat technique to optimize the initial population of nsga-iii. Additionally, it utilizes a multi-objective parallel strategy and an adaptive cross-variation strategy to enhance the convergence ability and robustness of nsga-iii, thereby preventing it from falling into a local optimum. The improved nsga-iii is employed to resolve the scheduling model under disparate operational conditions within the primary channel of Tianjin Port. The algorithm's efficacy is evaluated in comparison to the classical nsga-II and nsga-iii algorithms. The results show that the im-proved nsga-iii algorithm is better than the nsga-II and nsga-iii algorithms. The optimal scheduling plan for ship entry and exit in the main channel of Tianjin Port and the sequence of ship arrival and departure scheduling were obtained by Pareto solving set and combining with Topsis evaluation method. The method proposed in this paper offers a novel approach to the optimization of ship arrival and departure scheduling.

关键词： Marine vehicles Optimization Navigation Scheduling Seaports Optimal scheduling Genetic algorithms Scheduling model nsga-iii algorithm Tianjin Port waterway transport

来源：评论

学校读者我要写书评

暂无评论

Solving A Multi-objective Mission Planning Problem for UAV Swarms with An Improved nsga-iii algorithm

引用

INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2018年第1期11卷 1067-1081页

作者： Liu, Jiajie Wang, Weiping Li, Xiaobo Wang, Tao Bai, Senyang Wang, Yanfeng Natl Univ Def Technol Coll Syst Engn 137 Yanwachi Changsha 410073 Hunan Peoples R China

Restricted communication in unmanned aerial vehicle (UAV) swarms means that configuration needs to vary dynamically with changing tasks. We propose a mission planning model that uses a motif, a grouping of related functions, as the basic task unit. The planning model automatically generates a mission planning scheme from a task priority execution order given as an input. The selection of the best scheme from among possible solutions is a multi-objective optimization problem with calculation complexity rapidly increasing with the number of tasks. To address this difficulty, we enhance the nsga-iii algorithm by adding adaptive genetic operators when generating the offspring population. We apply the improved nsga-iii algorithm to optimize mission planning schemes with changing task priority execution orders. We validated the feasibility and effectiveness of the improved algorithm via a case study.

关键词： mission planning UAV swarms motif adaptive genetic operators nsga-iii algorithm optimization

来源：评论

学校读者我要写书评

暂无评论

An intelligent optimization method for the HCSB blanket based on an improved multi-objective nsga-iii algorithm and an adaptive BP neural network

引用

NUCLEAR ENGINEERING AND TECHNOLOGY 2023年第9期55卷 3150-3163页

作者： Zhou, Wen Sun, Guomin Miwa, Shuichiro Yang, Zihui Li, Zhuang Zhang, Di Wang, Jianye Univ Tokyo Sch Engn Dept Nucl Engn & Management 7-3-1 HongoBunkyo Ku Tokyo 1138654 Japan Chinese Acad Sci Hefei Inst Phys Sci Key Lab Neutron & Radiat Safety Hefei 230031 Anhui Peoples R China Univ Sci & Technol China Hefei 230027 Anhui Peoples R China Anhui Univ Inst Phys Sci & Informat Technol Hefei 230601 Peoples R China

To improve the performance of blanket: maximizing the tritium breeding rate (TBR) for tritium selfsufficiency, and minimizing the Dose of backplate for radiation protection, most previous studies are based on manual corrections to adjust the blanket structure to achieve optimization design, but it is difficult to find an optimal structure and tends to be trapped by local optimizations as it involves multiphysics field design, which is also inefficient and time-consuming process. The artificial intelligence (AI) maybe is a potential method for the optimization design of the blanket. So, this paper aims to develop an intelligent optimization method based on an improved multi-objective nsga-iii algorithm and an adaptive BP neural network to solve these problems mentioned above. This method has been applied on optimizing the radial arrangement of a conceptual design of CFETR HCSB blanket. Finally, a series of optimal radial arrangements are obtained under the constraints that the temperature of each component of the blanket does not exceed the limit and the radial length remains unchanged, the efficiency of the blanket optimization design is significantly improved. This study will provide a clue and inspiration for the application of artificial intelligence technology in the optimization design of blanket. & COPY;2023 Korean Nuclear Society, Published by Elsevier Korea LLC. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： CFETR HCSB blanket Radial arrangement Optimization design nsga-iii algorithm DE algorithm BP neural Network

来源：评论

学校读者我要写书评

暂无评论

nsga-iii algorithm for optimizing robot collaborative task allocation in the internet of things environment

引用

JOURNAL OF COMPUTATIONAL SCIENCE 2024年 81卷

作者： Shen, Jiazheng Tang, Saihong Ariffin, Mohd Khairol Anuar Mohd As'arry, A. Wang, Xinming Univ Putra Malaysia Fac Engn Serdang 43300 Malaysia

To improve the performance of intelligent products and reasonably distribute the load of the loading robot, a multi-objective, and multi-objective (Traveling-salesman-problem, TSP) mathematical model was established. A genetic algorithm based on speed invariant and the elite algorithm is proposed to solve the multi-TSP assignment problem. To ensure the integration of the population, a population resettlement strategy with elite lakes was proposed to improve the probability of population transfer to the best Pareto solution. The experiment verified that this strategy can approach the optimal solution more closely during the population convergence process, and compared it with traditional Multi TSP algorithms and single function multi-objective Multi TSP algorithms. By comparing the total distance and maximum deviation of multiple robot systems, it is proven that this algorithm can effectively balance the path length of each robot in task allocation. From the research results, it can be seen that in genetic algorithms, resetting the population after reaching precocity can maintain the optimization characteristics of the population and have a high probability of obtaining Pareto solutions. At the same time, storing elite individuals from previous convergent populations for optimization can better obtain Pareto solutions.

关键词： Internet of Things nsga-iii algorithm Multi -objective optimization Multi -robot scheduling

来源：评论

学校读者我要写书评

暂无评论

Automatic Pricing and Replenishment Strategy for Vegetable Products Based on Time Series and nsga-iii algorithm 3

Automatic Pricing and Replenishment Strategy for Vegetable P...

引用

3rd IEEE International Conference on Electrical Engineering, Big Data and algorithms, EEBDA 2024

作者： Li, Zhe He, Shufan Li, Zuchun Lu, Jia Wuhan Textile University Wuhan China

ISBN: (纸本)9798350380989

This paper focuses on the distribution pattern and interrelationship of the sales volume of each category and single product of vegetables, using the time series prediction model to forecast the cost and sales volume of different categories, using the Cook distance to analyze the outliers of the data. Setting up linear regression equations to fit the relationship between total sales volume and cost plus pricing of vegetables in each category, this paper establishes a nonlinear planning model of the time- series relationship, and gives the total daily replenishment volume and pricing strategy that makes the superstore benefit the most. © 2024 IEEE.

关键词： cook distance nonlinear programming models nsga-iii algorithm time series models

来源：评论

学校读者我要写书评

暂无评论

Energy-efficient virtual machine placement in distributed cloud using nsga-iii algorithm

引用

JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS 2023年第1期12卷 124-124页

作者： Gopu, Arunkumar Thirugnanasambandam, Kalaipriyan Rajakumar, R. Alghamdi, Ahmed Saeed Alshamrani, Sultan S. Maharajan, K. Rashid, Mamoon Dayananda Sagar Univ Dept Comp Sci & Engn Bangalore Karnataka India Vellore Inst Technol Ctr Smart Grid Technol Sch Comp Sci & Engn Chennai Campus Chennai Tamilnadu India Vellore Inst Technol Ctr Automat Sch Comp Sci & Engn Chennai Campus Chennai Tamilnadu India Taif Univ Coll Comp & Informat Technol Dept Comp Engn POB 11099 Taif 21994 Saudi Arabia Taif Univ Coll Comp & Informat Technol Dept Informat Technol POB 11099 Taif 21944 Saudi Arabia Kalasalingam Acad Res & Educ Sch Comp Dept Comp Sci & Engn Krishnankoil 626126 India Vishwakarma Univ Fac Sci & Technol Dept Comp Engn Pune 411048 India

Cloud computing is the most widely adapted computing model to process scientific workloads in remote servers accessed through the internet. In the IaaS cloud, the virtual machine (VM) is the execution unit that processes the user workloads. Virtualization enables the execution of multiple virtual machines (VMs) on a single physical machine (PM). Virtual machine placement (VMP) strategically assigns VMs to suitable physical devices within a data center. From the cloud provider's perspective, the virtual machine must be placed optimally to reduce resource wastage to aid economic revenue and develop green data centres. Cloud providers need an efficient methodology to minimize resource wastage, power consumption, and network transmission delay. This paper uses nsga-iii, a multi-objective evolutionary algorithm, to simultaneously reduce the mentioned objectives to obtain a non-dominated solution. The performance metrics (Overall Nondominated Vector Generation and Spacing) of the proposed nsga-iii algorithm is compared with other multi-objective algorithms, namely VEGA, MOGA, SPEA, and nsga-II. It is observed that the proposed algorithm performs 7% better that the existing algorithm in terms of ONVG and 12% better results in terms of spacing. ANOVA and DMRT statistical tests are used to cross-validate the results.

关键词： Cloud computing Distributed system Energy efficient Virtualization nsga-iii algorithm

来源：评论

学校读者我要写书评

暂无评论

Multi-objective ship weather routing method based on the improved nsga-iii algorithm

引用

JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION 2024年 38卷

作者： Ma, Dongfang Zhou, Siyuan Han, Yueyi Ma, Weihao Huang, Hongxun Zhejiang Univ Ocean Coll Zhoushan Peoples R China Peng Cheng Lab Shenzhen Peoples R China Key Lab Ocean Observat Imaging Testbed Zhejiang Pr Zhoushan Peoples R China Wuhan Univ Technol Sch Nav Yujiatou CampusNav Bldg 5004688 Youyi Rd Wuhan 430063 Peoples R China

This study investigates a multi-objective weather routing problem for ships, aiming to minimize operational costs, sailing time, and CO2 emissions simultaneously. Departing from conventional graph search algorithms based on nodes, we incorporate the ship's operational context, utilizing heading angle and speed as direct variables. Employing the Non-Dominated Sorting Genetic algorithm iii (nsga-iii) to solve the above problem, we observed convergence difficulties due to the traditional random initial population strategy. To address this, an improved nsga-iii is proposed, generating the initial population based on a route from a combination of sampling and the A* algorithm. The proposed method is applied to routes in the North Atlantic and the North Pacific, revealing that, compared to the commonly used A* and 3D Dijkstra methods, it can achieve over a 1.5 % reduction in ship operating costs, surpassing 6 % in certain scenarios. Additionally, we conducted an analysis of set coverage and inverted generational distance. The findings indicate that the improved nsga-iii algorithm exhibits superior convergence and diversity compared to the traditional nsga-iii algorithm.

关键词： Ship Weather routing Multi-objective optimization nsga-iii algorithm

来源：评论

学校读者我要写书评

暂无评论

Optimization of the nsga-iii algorithm Using Adaptive Scheduling

引用

ENGINEERING LETTERS 2023年第2期31卷 459页

作者： Zhang, Xijun Zhong, Yunfang Zhang, Baoqi Nie, Shengyuan Lanzhou Univ Technol Lanzhou 730050 Peoples R China

To address the nsga-iii algorithm's problems of low population diversity, high computational complexity, and slow convergence rate, in this paper an enhanced nsgaiii algorithm is proposed based on adaptive scheduling. The proposed algorithm increases the diversity of Pareto solutions by utilizing a crossover-mutation operator to adapt to dynamic changes of the population number during the population evolution stage. In addition, during the reference point generation process, the number of reference points is adjusted dynamically to avoid increasing the computational complexity under the conditions of numerous reference points and the screening function loss of population individuals caused by fewer reference points. The proposed algorithm is compared with the nsga-iii algorithm and the improved nsga-iii algorithm on the platEMO platform. Experimental results demonstrate that compared to the other algorithms, the proposed algorithm reduces the number of redundant reference points to a certain extent, increases the population diversity, and improves the convergence and distribution of the Pareto solution sets

关键词： Adaptive scheduling crossover mutation multi-objective optimization nsga-iii algorithm reference point generation

来源：评论

学校读者我要写书评

暂无评论

Charging station localization and sizing determination considering smart charging strategies based on nsga-iii and MOPSO

引用

SUSTAINABLE CITIES AND SOCIETY 2025年 122卷

作者： Li, Jiale Zhang, Yuxuan Wang, Xuefei Hebei Univ Technol Sch Civil & Transportat Engn Tianjin 300401 Peoples R China

The ownership of electric vehicles (EVs) has experienced a significant increase in recent years all over the world. However, the unmanaged and uncontrolled connection of a large number of EVs to the grid poses significant threats to grid stability and may result in heightened carbon emissions. This study introduces a smart charging scheduling method that concurrently takes into account charging costs, grid stability, and carbon emissions for EV users. This method is solved using the Non-dominated Sorting Genetic algorithm iii (nsga-iii). Based on the derived solution, three charging strategies were compared with data from four different countries with different energy structure. Both the total distance cost and total construction cost were considered to determine four options for the localization and sizing of EV CSs. The findings indicate that, in temporal terms, the optimal case for each strategy reduces charging costs, grid peak-valley difference, and carbon emissions by 6.66 %, 42.39 %, and 3.38 %, respectively. In spatial terms, the study elucidates the impact of various charging strategies on the localization and sizing of CSs. This study demonstrates the potential of an innovative method for long-term CS localization and sizing determination to provide direct guidance to management department.

关键词： Electric vehicle Carbon emissions Smart charging nsga-iii algorithm Localization and sizing of changing station

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：