检索结果-内蒙古大学图书馆

Hybrid of human learning optimization algorithm and particle swarm optimization algorithm with scheduling strategies for the flexible job-shop scheduling problem

引用

NEUROCOMPUTING 2020年 414卷 313-332页

作者： Ding, Haojie Gu, Xingsheng East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China

The flexible job-shop scheduling problem (FJSP) is a well-known combinational optimization problem. Studying FJSP is essential for promoting production efficiency and effectiveness. Different kinds of improved particle swarm optimization (PSO) algorithms have produced superior results for FJSP in the last few decades. Meanwhile, the human learning optimization (HLO) algorithm, a simple and adaptive learning algorithm for learning system, has helped improve algorithm performance by imitating human learning behavior in recent research. The study proposes a hybrid HLO-PSO algorithm, which utilizes various combinations of the proposed improved PSO and proposed scheduling strategies to solve FJSP under the algorithm architecture of HLO. With the guidance of HLO, the individual learning ability of every particle is further promoted based on the existed advantage of collective action decision of PSO;and with the help of rule-based scheduling strategies, the search capacity of the proposed improved PSO is also further enhanced. By the detailed exposition and analysis, the proposed HLO-PSO is easily implemented and embedded in other production system software or learning system software. Meanwhile, by using it to solve several groups of FJSP instances, the result comparisons with other related algorithms reveal that HLO-PSO can efficiently solve most of single-objective FJSP. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Human learning algorithm Adaptive learning system particle swarm optimization algorithm Flexible job-shop scheduling problem Scheduling strategy Operations research

来源：评论

学校读者我要写书评

暂无评论

An improved particle swarm optimization algorithm for frequency-selective surface design

引用

INDIAN JOURNAL OF PHYSICS 2020年第12期94卷 1909-1915页

作者： Sheng, Xianjun Gao, Xiang Liu, Ning Dalian Univ Technol Sch Elect Engn Dalian 116024 Peoples R China

In this paper, an improved particle swarm optimization (PSO) algorithm, which can perform a global search over the search space with a faster convergence speed, is proposed for frequency-selective surface (FSS) design. Four improvement strategies including better particle initialization, acceleration coefficient dynamic adjustment, search space reset, and particle density control are applied to PSO algorithm aiming to accelerate convergence and, at the same time, improve its search capability. For verification, the improved PSO algorithm has been successfully implemented to the square loop FSS design. Then, the optimized results are compared to those obtained by the standard PSO algorithm as well as those existing in the literature, respectively, proving that the improved PSO algorithm has the capacity for accelerating convergence on the premise of ensuring the optimization accuracy.

关键词： Frequency-selective surface particle swarm optimization algorithm Equivalent circuit model 41 20 Jb 52 25 Os 02 20 -a

来源：评论

学校读者我要写书评

暂无评论

Performance evaluation of the particle swarm optimization algorithm to unambiguously estimate plasma parameters from incoherent scatter radar signals

引用

EARTH PLANETS AND SPACE 2020年第1期72卷 1-25页

作者： Martinez-Ledesma, Miguel Jaramillo Montoya, Francisco Univ Santiago Chile Phys Dept Av Ecuador 3493 Santiago Chile Univ Concepcion CePIA Dept Astron Av Esteban Iturra S-NCasilla 160-C Concepcion Chile Univ Chile Dept Elect Engn Av Tupper 2007 Santiago Chile

Simultaneously estimating plasma parameters of the ionosphere presents a problem for the incoherent scatter radar (ISR) technique at altitudes between similar to 130 and similar to 300 km. Different mixtures of ion concentrations and temperatures generate almost identical backscattered signals, hindering the discrimination between plasma parameters. This temperature-ion composition ambiguity problem is commonly solved either by using models of ionospheric parameters or by the addition of parameters determined from the plasma line of the radar. Some studies demonstrated that it is also possible to unambiguously estimate ISR signals with very low signal fluctuation using the most frequently used non-linear least squares (NLLS) fitting algorithm. In a previous study, the unambiguous estimation performance of the particle swarm optimization (PSO) algorithm was evaluated, outperforming the standard NLLS algorithm fitting signals with very small fluctuations. Nevertheless, this study considered a confined search range of plasma parameters assuming a priori knowledge of the behavior of the ion composition and signals with very large SNR obtained at the Arecibo Observatory, which are not commonly feasible at other ISR facilities worldwide. In the present study, we conduct Monte Carlo simulations of PSO fittings to evaluate the performance of this algorithm at different signal fluctuation levels. We also determine the effect of adding different combinations of parameters known from the plasma line, different search ranges, and internal configurations of PSO parameters. Results suggest that similar performances are obtained by PSO and NLLS algorithms, but PSO has much larger computational requirements. The PSO algorithm obtains much lower convergences when no a priori information is provided. The a priori knowledge of N-e and Te/Ti\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{

关键词： Temperature-ion composition ambiguity Ionospheric plasma parameters Incoherent scatter radar Monte Carlo simulation optimization algorithms particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

particle swarm optimization algorithm for Power Scheduling Problem Using Smart Battery

Particle Swarm Optimization Algorithm for Power Scheduling P...

引用

IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT)

作者： Makhadmeh, Sharif Naser Khader, Ahamad Tajudin Al-Betar, Mohammed Azmi Naim, Syibrah Alyasseri, Zaid Abdi Alkareem Abasi, Ammar Kamal Univ Sains Malaysia Sch Comp Sci George Town Malaysia Al Balqa Appl Univ Dept Informat Technol Irbid Jordan Univ Kufa ECE Dept Fac Engn Najaf Iraq

ISBN: (纸本)9781538679425

Power Scheduling Problem (PSP) is a problem of schedule the smart home appliances at appropriate time period according to an electricity pricing scheme. The smart home appliances can be scheduled by shifting their time operations from period to another. The significant objective of the scheduling process is to reduce the electricity bill and Peak-to-average ratio (PAR) and improve the user comfort level. In this paper, particle swarm optimization (PSO) algorithm is adapted in order to handle the PSP and to obtain an optimal smart home appliances schedule. Smart battery (SB) is formulated and used in this work to enhance the schedule of the appliances by storing the power at low peak periods and use the stored power by the smart home appliances at peak periods. The simulation results proved the efficiency of using the proposed SB in terms of reducing electricity bill and improving the user comfort level. In addition, PSO is compared with genetic algorithm (GA) in order to evaluate its performance. PSO outperforms GA in terms of achieving the PSP objectives.

关键词： optimization particle swarm optimization algorithm Power Scheduling Problem Smart Battery

来源：评论

学校读者我要写书评

暂无评论

Design and experimental investigation on VL-MLI intended for half height (H-H) method to improve power quality using modified particle swarm optimization (MPSO) algorithm

引用

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第6期42卷 5939-5956页

作者： Ramaraju, Satish Kumar Kaliannan, Thenmalar Joseph, Sheela Androse Kumaravel, Umadevi Albert, Johny Renoald Natarajan, Arun Vignesh Chellakutty, Gokul Prasad Sengunthar Coll Engn Dept Med Elect Engn Namakkal Tamil Nadu India Vivekanandha Coll Engn Women Dept EEE Namakkal Tamil Nadu India Kongu Engn Coll Dept EEE Perundurai Tamil Nadu India Sengunthar Engn Coll Dept EEE Namakkal Tamil Nadu India Gokaraju Rangaraju Inst Engn & Technol Dept ECE Hyderabad Telangana India SNS Coll Engn Dept ECE Coimbatore Tamil Nadu India

A Voltage lift performance is an excellent role to DC/DC conversion topology. The Voltage Lift Multilevel Inverter (VL-MLI) topology is suggested with minimal number of components compared to the conventional multilevel inverter (MLI). In this method, the Modified particle swarm optimization (MPSO) conveys a primary task for the VL-MLI using Half Height (H-H) method, it determine the required optimum switching angles to eliminate desired value of harmonics. The simulation circuit for fifteen level output uses single switch voltage-lift inverter fed with resistive and inductive loads (R & L load). The power quality is developed by voltage-lift multilevel inverter with minimized harmonics under the various Modulation Index (MI) while varied from 0.1 up to 1. The circuit is designed in a Field Programmable Gate Array (FPGA), which includes the MPSO rules for fast convergence to reduce the lower order harmonics and finds the best optimum switching angle values. To report this problem the H-H has implemented with MPSO to reduce minimum Total Harmonic Distortion (THD) for simulation circuit using Proteus 7.7 simulink tool. Due to the absence of multiple switches, filter and inductor element exposes for novelty of the proposed system. The comparative analysis has been carried-out with existing optimization and modulation methods.

关键词： Solar-Photovoltaic voltage lift-multilevel inverter particle swarm optimization algorithm half height field program gate array

来源：评论

学校读者我要写书评

暂无评论

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

引用

SENSORS 2021年第18期21卷 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

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

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

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

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

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：