检索结果-内蒙古大学图书馆

Multi-Objective and Parallel particle swarm optimization algorithm for Container-Based Microservice Scheduling

SENSORS 2021年第18期21卷 6212-6212页

作者： Chen, Xinying Xiao, Siyi Dalian Jiaotong Univ Sch Software Dalian 116000 Peoples R China

An application based on a microservice architecture with a set of independent, fine-grained modular services is desirable, due to its low management cost, simple deployment, and high portability. This type of container technology has been widely used in cloud computing. Several methods have been applied to container-based microservice scheduling, but they come with significant disadvantages, such as high network transmission overhead, ineffective load balancing, and low service reliability. In order to overcome these disadvantages, in this study, we present a multi-objective optimization problem for container-based microservice scheduling. Our approach is based on the particle swarm optimization algorithm, combined parallel computing, and Pareto-optimal theory. The particle swarm optimization algorithm has fast convergence speed, fewer parameters, and many other advantages. First, we detail the various resources of the physical nodes, cluster, local load balancing, failure rate, and other aspects. Then, we discuss our improvement with respect to the relevant parameters. Second, we create a multi-objective optimization model and use a multi-objective optimization parallel particle swarm optimization algorithm for container-based microservice scheduling (MOPPSO-CMS). This algorithm is based on user needs and can effectively balance the performance of the cluster. After comparative experiments, we found that the algorithm can achieve good results, in terms of load balancing, network transmission overhead, and optimization speed.

关键词： multi-objective optimization container-based microservice scheduling particle swarm optimization algorithm cloud computing

来源：评论

学校读者我要写书评

暂无评论

Research on building truss design based on particle swarm intelligence optimization algorithm

引用

INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2022年第SUPPL 1期13卷 38-48页

作者： Sun, Yulong Li, Hongjuan Shabaz, Mohammad Sharma, Amit Yellow River Conservancy Tech Inst Kaifeng 075004 Henan Peoples R China Chitkara Univ Dept Comp Sci Engn Rajpura 140401 Punjab India

optimization methodologies are being utilized in various structural designing practices to solve size, shape and topology optimization problems. A heuristic particle swarm optimization (HPSO) algorithm was anticipated in this article in order to address the size optimization problem of truss with stress and displacement constraints. This article contributes in improvisation in the truss structure design rationality while reducing the engineering cost by proposing the HPSO approach. Primarily, the basic principle of the original PSO algorithm is presented, then the compression factor is established to improve the PSO algorithm, and a reasonable parameter setting value is presented. To validate the performance of the proposed optimization approach, various experimental illustrations were performed. The results show that the convergence history of experimental illustration 2 and experimental illustration 3 is optimal. The experimental illustration 2 converges after about 150 iterations, however, the experimental illustration 3 is close to the optimal solution after about 500 iterations. Therefore, the PSO algorithm can successfully optimize the size design of truss structures, and the algorithm is also time efficient. The improved PSO algorithm has good convergence and stability, and can effectively optimize the size design of truss structures.

关键词： particle swarm optimization algorithm Truss structure Compression factor Heuristic particle swarm optimization Engineering cost Topology optimization problems

来源：评论

学校读者我要写书评

暂无评论

particle swarm optimization algorithm with Variety Inertia Weights to Solve Unequal Area Facility Layout Problem

Particle Swarm Optimization Algorithm with Variety Inertia W...

引用

第32届中国控制与决策会议

作者： Jia-Li Zhou Jie-Sheng Wang Yong-Xin Zhang Qing-Sheng Guo Hui Li Yi-Xuan Lu School of Electronic and Information Engineering University of Science & Technology Liaoning

The unequal area facility layout problem(UA-FLP) is to place some objects in a specified space according to certain requirements,which is a NP-hard problem in mathematics because of the complexity of its solution,the combination explosion and the complexity of engineering *** swarm optimization(PSO) algorithm is a kind of swarm intelligence algorithm by simulating the predatory behavior of *** at the minimization of material handling cost and the maximization of workshop area utilization,the optimization mathematical model of UA-FLPP is established,and it is solved by the particle swarm optimization(PSO) algorithm which simulates the design of birds' predation *** improved PSO algorithm is constructed by using nonlinear inertia weight,dynamic inertia weight and other methods to solve static unequal area facility layout *** effectiveness of the proposed method is verified by simulation experiments.

关键词： particle swarm optimization algorithm Inertia Weight Unequal Area Facility Layout Problem

来源：评论

学校读者我要写书评

暂无评论

optimization of Agricultural Machinery Allocation in Heilongjiang Reclamation Area Based on particle swarm optimization algorithm

引用

TEHNICKI VJESNIK-TECHNICAL GAZETTE 2021年第6期28卷 1885-1893页

作者： Liu, Li Chen, Tong Gao, Shijie Liu, Ye Yang, Shuguo Wang, Xinli Heilongjiang Bayi Agr Univ Coll Engn Xinyang Rd Daqing 163319 Heilongjiang Peoples R China Heilongjiang Bayi Agr Univ Coll Econ & Management Xinyang Rd Daqing 163319 Heilongjiang Peoples R China Heilongjiang Jianshan Farm Co Ltd Beidahuang Grp Nenjiang 161499 Heilongjiang Peoples R China

Aiming at the imbalance of seasonal agricultural machinery operations in different regions and the low efficiency of agricultural machinery, an experiment is proposed to use particle swarm algorithm to plan agricultural machinery paths to solve the current problems in agricultural machinery operations. Taking the harvesting of autumn soybeans at Jianshan Farm in Heilongjiang Reclamation Area as the experimental object, this paper constructs the optimization target model of the maximum net income of farm machinery households, and uses particle swarm algorithm to carry out agricultural machinery operation distribution and path planning gradually. In this paper, by introducing 0 -1 mapping, the improved algorithm adopts continuous decision variables to solve the optimization of discrete variables in agricultural machinery operations. The test results show that the particle swarm algorithm can realize the optimal allocation of agricultural machinery path, and the particle swarm algorithm is scientific and explanatory to solve the agricultural machinery allocation problem. This research can provide a scientific basis for farm agricultural machinery allocation and decision analysis.

关键词： agricultural machinery allocation particle swarm optimization algorithm path planning

来源：评论

学校读者我要写书评

暂无评论

Prediction of the splitting tensile strength of the bonding interface by combining the support vector machine with the particle swarm optimization algorithm

引用

ENGINEERING STRUCTURES 2021年 230卷 111696-111696页

作者： Jiang, Wei Xie, Youjun Li, Wenxu Wu, Jianxian Long, Guangcheng Cent South Univ Sch Civil Engn Changsha 410075 Peoples R China Changjiu Intercity Railway Ltd Co Nanchang 330002 Jiangxi Peoples R China

The bonding performance of the interface between two concrete layers is of great importance for China Rail Track System III (CRTS III) slab ballastless track structure. While the bonding performance is close to the distribution and amounts of defects such as the bubble or the void on the bonding interface. In this paper, combining the Support Vector Machine (SVM) with the particle swarm optimization (PSO) algorithm, named SVM-PSO algorithm is used to predict the splitting tensile strength of the bonding interface based on the distribution of defects. And the influence of different parameters in the SVM-PSO algorithm on the prediction ability is discussed. Results indicate that the relative error between the average strength value of every 100 predictions and the experimental strength value is less than 5%. The achievements will provide an effective method for predicting the splitting tensile strength of the bonding interface between two concrete layers like CRTS III slab ballastless track structure in practice.

关键词： Splitting tensile strength of the bonding interface Support vector machine particle swarm optimization algorithm Prediction CRTS III slab ballastless track structure

来源：评论

学校读者我要写书评

暂无评论

particle swarm optimization algorithm with compression factor is used to solve complex shading MPPT problem

Particle swarm optimization algorithm with compression facto...

引用

作者： Yanping Wang Zhen Zhang School of Electrical Engineering Zhengzhou University

In photovoltaic power generation systems, pv arrays are often affected by local shadow phenomena, resulting in the unstable operation of the system and the reduction of output power. In addition, pv array's p-u characteristic curve will show multiple peaks, and the traditional maximum power point tracking(MPPT) algorithm cannot complete the tracking of the maximum power point because it can only find the single peak. particle swarm optimization(PSO) algorithm has good global optimization ability of multi-peak, which is widely used in tracking the maximum power point of local shadow. However, PSO algorithm has the shortcoming of insufficient convergence speed and low search accuracy. Therefore, a particle swarm optimization(YSPSO) algorithm with compression factor is proposed to effectively improve the global search ability and local improvement ability of the whole algorithm.

关键词： MPPT problem particle swarm optimization algorithm compression factor

来源：评论

学校读者我要写书评

暂无评论

Determine the optimal switching angles symmetrical cascaded multilevel inverter using Improved particle swarm optimization algorithm 18

Determine the optimal switching angles symmetrical cascaded ...

引用

18th IEEE World Symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Ahmadi, Sajjad Sahnesaraie, Mostafa Andalib Talami, Saeid Hojjati Dini, Fakhteh Zanganeh, Mahnaz Mohebbi Ashgevari, Yazdan Islamic Azad Univ Dept Elect Engn Ardabil Sci & Res Branch Ardebil Iran Alborz Elect Power Distribut Alborz Iran Islamic Azad Univ Dept Elect Engn Mehriz Branch Mehriz Iran Shahid Beheshti Univ Fac Elect & Comp Engn Tehran Iran Islamic Azad Univ Dept Elect Engn Ardabil Branch Ardebil Iran

ISBN: (纸本)9781728131498

With the increasing use of inverters in the industry, the need to reduce the output harmonics is felt more than ever. Interstellar multi-level structures have grown exponentially due to benefits such as harmonic reduction and loss reduction. In multi-level inverters, nonlinear and complex equations become more complex when the number of surfaces increases. One of the ways to find optimal keying angles is to reduce the harmonics of the whole Newton-Raphson method. One of the main disadvantages of the Newton-Raphson method is the strong dependence of the answers to the initial guesses. By choosing different initial guesses, different answers may be obtained or results will not converge. And their solution is not possible by conventional methods of numerical solution of nonlinear equations such as the Newton-Ruffson method. In this thesis, one of a variety of Intelligent algorithms is used to solve the Newton-Raphson method problem. The particle swarm algorithm has been selected based on the proper background in solving problems of high complexity to solve the problem of optimal keying angles in this dissertation. In this thesis, the particle swarm algorithm has been improved. Simulation results for various multilevel converters have been presented and represent the high efficiency of the improved type of particle swarm algorithm in determining the appropriate angle of fire for reducing the high harmonics and generating a waveform with very low harmonic distortion and near sinusoidal.

关键词： Multilevel Inverters Selective Harmonic Elimination particle swarm optimization algorithm Improved particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Low-delay layout planning based on improved particle swarm optimization algorithm in 5G optical fronthaul network

引用

OPTICAL FIBER TECHNOLOGY 2021年 67卷

作者： Wang, Nan Liu, Jianfei Lu, Jia Zeng, Xiangye Zhao, Xinyu Hebei Univ Technol Sch Elect & Informat Engn Tianjin 300401 Peoples R China Tianjin Key Lab Elect Mat & Devices Tianjin 300401 Peoples R China Hebei Key Lab Adv Laser Technol & Equipment Tianjin 300401 Peoples R China

The rapid development of time-sensitive applications, such as autopilot, telemedicine and industrial automation, demands more stringent requirements on end-to-end delay. Therefore, low-delay planning of 5G networks is supposed to be a crucial stage. In this paper, a low-delay layout planning based on the nonlinear decreasing inertia weight particle swarm optimization algorithm (NL-wPSO) is demonstrated. By iteratively calculating the velocity and position of each particle, the proposed algorithm can optimize the layout of AAUs in the fronthaul network. Thereby, the distance between the AAUs and edge computing server will be shortened. Compared to the basic PSO, the simulation results indicate a significant reduction on transmission delay.

关键词： 5G Layout planning Edge computing service particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

A new bifuzzy optimization method for remanufacturing scheduling using extended discrete particle swarm optimization algorithm

引用

COMPUTERS & INDUSTRIAL ENGINEERING 2021年 156卷 107219-107219页

作者： Shi, Jiaxuan Zhang, Wenyu Zhang, Shuai Chen, Jie Zhejiang Univ Finance & Econ Sch Informat Management & Artificial Intelligence Hangzhou 310018 Peoples R China

As the energy shortage and environment pollution become increasingly deteriorating, remanufacturing has become a hot research field for its energy-saving and environmental advantages. In remanufacturing systems, scheduling is a key step that directly affects the successful realization of remanufacturing benefits. Since the remanufacturing involves many two-fold uncertainties, scheduling for remanufacturing is more challenging than for traditional manufacturing. However, few studies have addressed these two-fold uncertainties in remanufacturing environments. Thus, this study proposes a new bifuzzy remanufacturing scheduling model, which handles many two-fold uncertainties in remanufacturing and applies the bifuzzy variable to describe these uncertainties. Multiple non-identical parallel processing lines for remanufacturing end-of-life products have also been integrated into the proposed model. An extended discrete particle swarm optimization algorithm with an effective representation scheme is proposed to solve the model. In the presented algorithm, three new guiding directions and a new multi-directional guiding strategy are designed to enhance the diversity of population, and a new position updating mechanism, local search strategy, and anti-stagnation mechanism are integrated to improve the algorithmic performance. Experiments are performed to verify the effectiveness of the proposed model by comparing it with the traditional deterministic model, and the effectiveness of the proposed algorithm in solving this model by comparing it with other baseline algorithms.

关键词： Remanufacturing scheduling Bifuzzy theory Bifuzzy variable Multiple non-identical parallel processing lines particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Improved subgroup method coupled with particle swarm optimization algorithm for intra-pellet non-uniform temperature distribution problem

引用

ANNALS OF NUCLEAR ENERGY 2021年 153卷 108070-108070页

作者： Li, Song Zhang, Qian Zhang, Zhijian Zhao, Qiang Liang, Liang Harbin Engn Univ Fundamental Sci Nucl Safety & Simulat Technol Lab Coll Nucl Sci & Technol 145 Nantong St Harbin 150001 Heilongjiang Peoples R China

Due to the Doppler broadening effect, the conventional subgroup weights of different temperatures correspond to different energy ranges. Therefore, the conventional subgroup fixed source equation could not be established without approximation between different temperatures. In this study, the improved subgroup method coupled with the particle swarm optimization algorithm (ISPSO) is proposed. The subgroup fixed source equation is solved for different temperatures by sharing the same set of subgroup weights, which are calculated by generationally updating the particle position during the particle swarm optimization. Afterward, the partial subgroup levels are obtained by the least square method. Besides, ISPSO adopts the fine mesh structure for the resonance interference treatment, then the multigroup effective resonance cross section is obtained by the group condensation. The subgroup parameters of ISPSO could be either generated online or pre-determined. The numerical verification indicates that ISPSO could accurately handle the resonance treatment for the complicated temperature conditions. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Resonance self-shielding Subgroup method Subgroup parameters Non-uniform temperature distribution particle swarm optimization algorithm

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：