检索结果-南通市图书馆

Adaptive candidate estimation-assisted multi-objective particle swarm optimization

Science China(Technological Sciences) 2022年第8期65卷 1685-1699页

作者： HAN HongGui ZHANG LinLin HOU Ying QIAO JunFei Faculty of Information Technology Beijing University of TechnologyBeijing 100124China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing 100124China Engineering Research Center of Digital Community Ministry of EducationBeijing 100124China

The selection of global best(Gbest) exerts a high influence on the searching performance of multi-objective particle swarm optimization algorithm(MOPSO). The candidates of MOPSO in external archive are always estimated to select Gbest. However,in most estimation methods, the candidates are considered as the Gbest in a fixed way, which is difficult to adapt to varying evolutionary requirements for balance between convergence and diversity of MOPSO. To deal with this problem, an adaptive candidate estimation-assisted MOPSO(ACE-MOPSO) is proposed in this paper. First, the evolutionary state information,including both the global dominance information and global distribution information of non-dominated solutions, is introduced to describe the evolutionary states to extract the evolutionary requirements. Second, an adaptive candidate estimation method,based on two evaluation distances, is developed to select the excellent leader for balancing convergence and diversity during the dynamic evolutionary process. Third, a leader mutation strategy, using the elite local search(ELS), is devised to select Gbest to improve the searching ability of ACE-MOPSO. Fourth, the convergence analysis is given to prove the theoretical validity of ACE-MOPSO. Finally, this proposed algorithm is compared with popular algorithms on twenty-four benchmark functions. The results demonstrate that ACE-MOPSO has advanced performance in both convergence and diversity.

关键词： multi-objective particle swarm optimization evolutionary state information adaptive candidate estimation convergence and diversity convergence analysis

来源：

维普期刊数据库

同方期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

A modified multi-objective particle swarm optimization approach and its application to the design of a deepwater composite riser

引用

Acta Mechanica Sinica 2018年第2期34卷 275-284页

作者： Y.Zheng J.Chen Department of Mechanics Huazhong University of Science and TechnologyWuhan 430074China Hubei Key Laboratory for Engineering Structural Analysis and Safety Assessment Wuhan 430074China

A modified multi-objective particle swarm optimization method is proposed for obtaining Pareto-optimal solutions effectively. Different from traditional multiobjective particle swarm optimization methods, Kriging meta-models and the trapezoid index are introduced and integrated with the traditional one. Kriging meta-models are built to match expensive or black-box functions. By applying Kriging meta-models, function evaluation numbers are decreased and the boundary Pareto-optimal solutions are identified rapidly. For bi-objective optimization problems, the trapezoid index is calculated as the sum of the trapezoid’s area formed by the Pareto-optimal solutions and one objective axis. It can serve as a measure whether the Pareto-optimal solutions converge to the Pareto front. Illustrative examples indicate that to obtain Paretooptimal solutions, the method proposed needs fewer function evaluations than the traditional multi-objective particle swarm optimization method and the non-dominated sorting genetic algorithm II method, and both the accuracy and the computational efficiency are improved. The proposed method is also applied to the design of a deepwater composite riser example in which the structural performances are calculated by numerical analysis. The design aim was to enhance the tension strength and minimize the cost. Under the buckling constraint, the optimal trade-off of tensile strength and material volume is obtained. The results demonstrated that the proposed method can effec tively deal with multi-objective optimizations with black-box functions.

关键词： multi-objective particle swarm optimization Kriging meta-model Trapezoid index Deepwater composite riser

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Optimal placement of distributed generation units in distribution systems via an enhanced multi-objective particle swarm optimization algorithm

引用

Journal of Zhejiang University-Science C(Computers and Electronics) 2014年第4期15卷 300-311页

作者： Shan CHENG Min-you CHEN Rong-jong WAI Fang-zong WANG College of Electrical Engineering and New Energy China Three Gorges University State Key Laboratory of Power Transmission Equipment & System Security and New Technology Department of Electrical Engineering and Fuel Cell Center Yuan Ze University

This paper deals with the optimal placement of distributed generation(DG) units in distribution systems via an enhanced multi-objective particle swarm optimization(EMOPSO) algorithm. To pursue a better simulation of the reality and provide the designer with diverse alternative options, a multi-objective optimization model with technical and operational constraints is constructed to minimize the total power loss and the voltage fluctuation of the power system simultaneously. To enhance the convergence of MOPSO, special techniques including a dynamic inertia weight and acceleration coefficients have been integrated as well as a mutation operator. Besides, to promote the diversity of Pareto-optimal solutions, an improved non-dominated crowding distance sorting technique has been introduced and applied to the selection of particles for the next iteration. After verifying its effectiveness and competitiveness with a set of well-known benchmark functions, the EMOPSO algorithm is employed to achieve the optimal placement of DG units in the IEEE 33-bus system. Simulation results indicate that the EMOPSO algorithm enables the identification of a set of Pareto-optimal solutions with good tradeoff between power loss and voltage stability. Compared with other representative methods, the present results reveal the advantages of optimizing capacities and locations of DG units simultaneously, and exemplify the validity of the EMOPSO algorithm applied for optimally placing DG units.

关键词： Distributed generation multi-objective particle swarm optimization Optimal placement Voltage stability index Power loss

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

An Improved multi-objective particle swarm optimization Routing on MANET

引用

Computer Systems Science & Engineering 2023年第2期44卷 1187-1200页

作者： G.Rajeshkumar M.Vinoth Kumar K.Sailaja Kumar Surbhi Bhatia Arwa Mashat Pankaj Dadheech Department of Computer Science and Engineering Erode Sengunthar Engineering CollegePerunduraiErode638057Tamil NaduIndia Department of Information Science and Engineering Dayananda Sagar Academy of Technology and ManagementBangalore560082India Department of Master of Computer Applications M.S.Ramaiah Institute of TechnologyBangalore560054India Department of Information Systems College of Computer Science and Information TechnologyKing Faisal UniversityRiyadh11533Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityRabigh21911Saudi Arabia Department of Computer Science and Engineering Swami Keshvanand Institute of TechnologyManagement&GramothanJaipur302017RajasthanIndia

A Mobile Ad hoc Network(MANET)is a group of low-power con-sumption of wireless mobile nodes that conﬁgure a wireless network without the assistance of any existing infrastructure/centralized *** primary aim of MANETs is to extendﬂexibility into the self-directed,mobile,and wireless domain,in which a cluster of autonomous nodes forms a MANET routing *** Intrusion Detection System(IDS)is a tool that examines a network for mal-icious behavior/policy violations.A network monitoring system is often used to report/gather any suspicious attacks/*** IDS is a software program or hardware system that monitors network/security trafﬁc for malicious attacks,sending out alerts whenever it detects malicious *** impact of Dynamic Source Routing(DSR)in MANETs challenging blackhole attack is investigated in this research *** Cluster Trust Adaptive Acknowledgement(CTAA)method is used to identify unauthorised and malfunctioning nodes in a MANET *** system is active and provides successful delivery of a data packet,which implements Kalman Filters(KF)to anticipate node ***,KF is used to eliminate synchronisation errors that arise during the sending and receiving *** order to provide an energy-efﬁcient solution and to minimize network trafﬁc,route optimization in MANET by using multi-objective particle swarm optimization(MOPSO)technique to determine the optimal num-ber of clustered MANET along with energy dissipation in *** to the researchﬁndings,the proposed CTAA-MPSO achieves a Packet Delivery Ratio(PDR)of 3.3%.In MANET,the PDR of CTAA-MPSO improves CTAA-PSO by 3.5%at 30%malware.

关键词： MANET intrusion detection system cluster kalmanﬁlter dynamic source routing multi-objective particle swarm optimization packet delivery ratio

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Credible Web Service Composition based on Improved multi-objective particle swarm optimization

Credible Web Service Composition based on Improved Multi-obj...

引用

第40届中国控制会议

作者： Feng Liu Min Han Jianwei Liu Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Key Laboratory of Intelligent Control and optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Faculty of Infrastructure Engineering Dalian University of Technology

In the field of Web service composition,since the published value of Quality of Service(QoS) is not necessarily reliable,the research on the credibility of Web services has become a research ***,the current credibility research cannot well describe the difference between the published value and the actual operating *** the same time,the algorithm used to solve the Web service composition scheme generally has the problem of poor solution *** on this,this paper proposes a credible Web service composition method based on improved multi-objective particle swarm optimization(IMOPSO).Firstly,this paper proposes a similarity-based credibility calculation model,and combines it with the QoS published value through the inverse of the similarity,so that the QoS of the Web service meets the actual operation ***,this paper proposes an improved multi-objective particle swarm optimization algorithm,which solves the problem of premature convergence by expanding the population diversity,and the solution effect is significantly ***,the improved multi-objective particle swarm algorithm is used to solve the Web service composition problem based on credible Qo *** experiments on the QWS2 data set and the Synthetic data set prove that the method proposed in this paper can effectively improve the credibility of the service composition scheme,and has higher composition efficiency and better composition effect.

关键词： credibility Web service composition QoS multi-objective particle swarm optimization

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

multiple objective particle swarm optimization technique for economic load dispatch

引用

Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2005年第5期6卷 420-427页

作者：赵波曹一家 School of Electrical Engineering Zhejiang University Hangzhou 310027 China

A multi-objective particle swarm optimization (MOPSO) approach for multi-objective economic load dispatch problem in power system is presented in this paper. The economic load dispatch problem is a non-linear constrained multi-objective optimization problem. The proposed MOPSO approach handles the problem as a multi-objective problem with competing and non-commensurable fuel cost, emission and system loss objectives and has a diversity-preserving mechanism using an external memory (call “repository”) and a geographically-based approach to find widely different Pareto-optimal solutions. In addition, fuzzy set theory is employed to extract the best compromise solution. Several optimization runs of the proposed MOPSO approach were carried out on the standard IEEE 30-bus test system. The results revealed the capabilities of the proposed MOPSO approach to generate well-distributed Pareto-optimal non-dominated solutions of multi-objective economic load dispatch. Com- parison with multi-objective Evolutionary Algorithm (MOEA) showed the superiority of the proposed MOPSO approach and confirmed its potential for solving multi-objective economic load dispatch.

关键词： Economic load dispatch multi-objective optimization multi-objective particle swarm optimization

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Analytic design of information granulation-based fuzzy radial basis function neural networks with the aid of multiobjective particle swarm optimization

引用

International Journal of Intelligent Computing and Cybernetics 2012年第1期5卷 4-35页

作者： Byoung-Jun Park Jeoung-Nae Choi Wook-Dong Kim Sung-Kwun Oh IT Convergence Technology Research Laboratory Electronics and Telecommunications Research Institute(ETRI)DaejeonSouth Korea Research Institute KDT Co.LtdBucheong-siSouth Korea Department of Electrical Engineering The University of SuwonHwaseong-siSouth Korea

Purpose–The purpose of this paper is to consider the concept of Fuzzy Radial Basis Function Neural Networks with Information Granulation(IG-FRBFNN)and their optimization realized by means of the multiobjective particle swarm optimization(MOPSO).Design/methodology/approach–In fuzzy modeling,complexity,interpretability(or simplicity)as well as accuracy of the obtained model are essential design *** the performance of the IG-RBFNN model is directly affected by some parameters,such as the fuzzification coefficient used in the FCM,the number of rules and the orders of the polynomials in the consequent parts of the rules,the authors carry out both structural as well as parametric optimization of the network.A multi-objective particle swarm optimization using Crowding Distance(MOPSO-CD)as well as O/WLS learning-based optimization are exploited to carry out the structural and parametric optimization of the model,respectively,while the optimization is of multiobjective character as it is aimed at the simultaneous minimization of complexity and maximization of ***–The performance of the proposed model is illustrated with the aid of three *** proposed optimization method leads to an accurate and highly interpretable fuzzy ***/value–A MOPSO-CD as well as O/WLS learning-based optimization are exploited,respectively,to carry out the structural and parametric optimization of the *** a result,the proposed methodology is interesting for designing an accurate and highly interpretable fuzzy model.

关键词： Modelling optimization techniques Neural nets Design calculations Fuzzy c-means clustering multi-objective particle swarm optimization Information granulation-based fuzzy radial basis function neural network Ordinary least squaresmethod Weighted least square method

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Layout problem of multi-component systems arising for improving maintainability

引用

Journal of Central South University 2014年第5期21卷 1833-1841页

作者：罗旭杨拥民葛哲学温熙森官凤娇 Science and Technology on Integrated Logistics Support Laboratory National University of Defense Technology

To improve the mainlainability design efficiency and quality, a layout optimization method for maintainability of multi-component systems was proposed. The impact of the component layout design on system maintainability was analyzed, and the layout problem for maintainability was presented. It was formulated as an optimization problem, where maintainability, layout space and distance requirement were formulated as objective functions. A multi-objective particle swarm optimization algorithm, in which the constrained-domination relationship and the update strategy of the global best were simply modified, was then used to obtain Pareto optimal solutions for the maintainability layout design problem. Finally, application in oxygen generation system of a spacecraft was studied in detail to illustrate the effectiveness and usefulness of the proposed method. The results show that the concurrent maintainability design can be carried out during the layout design process by solving the layout optimization problem for maintainability.

关键词： maintainability layout problem optimization multi-component system multi-objective particle swarm optimization

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Optimizing the lattice design of a diffraction-limited storage ring with a rational combination of particle swarm and genetic algorithms

引用

Chinese Physics C 2017年第2期41卷 166-176页

作者：焦毅徐刚 Key Laboratory of particle Acceleration Physics and Technology Institute of High Energy Physics Chinese Academy of Sciences Beijing 100049 China

In the lattice design of a diffraction-limited storage ring（DLSR） consisting of compact multi-bend achromats（MBAs）, it is challenging to simultaneously achieve an ultralow emittance and a satisfactory nonlinear performance, due to extremely large nonlinearities and limited tuning ranges of the element parameters. Nevertheless, in this paper we show that the potential of a DLSR design can be explored with a successive and iterative implementation of the multi-objective particle swarm optimization（MOPSO） and multi-objective genetic algorithm（MOGA）. For the High Energy Photon Source, a planned kilometer-scale DLSR, optimizations indicate that it is feasible to attain a natural emittance of about 50 pm·rad, and simultaneously realize a sufficient ring acceptance for on-axis longitudinal injection, by using a hybrid MBA lattice. In particular, this study demonstrates that a rational combination of the MOPSO and MOGA is more effective than either of them alone, in approaching the true global optima of an explorative multi-objective problem with many optimizing variables and local optima.

关键词： diffraction-limited storage ring High Energy Photon Source multi-objective particle swarm optimization multi-objective genetic algorithm lattice design

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

multi-objective optimization-based prediction of excavation-induced tunnel displacement

引用

Underground Space 2022年第5期7卷 735-747页

作者： Yuanqin Tao Wei He Honglei Sun Yuanqiang Cai Junqiang Chen Research Center of Coastal and Urban Geotechnical Engineering College of Civil Engineering and ArchitectureZhejiang UniversityHangzhou 310058China Institute of Geotechnical Engineering College of Civil EngineeringZhejiang University of TechnologyHangzhou 310000China Institute of Rock and Soil Mechanics Chinese Academy of SciencesWuhan 430061China

This paper proposes an inverse method for improving the prediction of tunnel displacements during adjacent *** this framework,staged data assimilation and parameter identification are conducted using the multi-objective particle swarm optimization *** monitoring data are assumed to be more informative and assigned more weights in the multi-objective optimization to improve the prediction ***,an empirical formula is applied to correct the time effect of tunnel *** Kriging method is introduced to surrogate the finite element model to reduce computational *** proposed framework is verified using a typical staged“excavation nearing tunnel”*** predictions using the updated parameters are in good agreement with the *** identified values of underlying soil parameters are within the typical ranges for the unloading *** updated time effect indicates that tunnel displacements may develop excessively in the three months after the region S1-B is excavated to the *** maximum vertical tunnel displacement may increase from the currently measured 12 mm to the calculated 26 mm if the later construction is suspended long *** constructions need to be timely conducted to restrain the time effect and control tunnel displacements.

关键词： Tunnel displacement Excavation Time effect multi-objective particle swarm optimization Parameter identification

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

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

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

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

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

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：