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IEEE International Conference on Intelligent Transportation Systems

作者： Jun Liu Tianyun Shi Inst. of Comput. Technol. Beijing China

ISBN: (纸本)9781479960798

Smart railway trajectory planning is one of the most interesting and important topics in the fields of intelligent railway transportation, computer science and evolutionary computation, etc. Several objectives and constraints of railway trajectory planning problem are closely related to the distance factor, the construction cost, the altitude factor and the comfortability, etc. More importantly, the new particle swarm optimization (PSO) algorithm, including two optimization sub-stages, firstly optimize the coordinate in y-axis and secondly optimize the latitude in z-axis to design the optimal 3-D railway trajectory. Numerical results highlight that the new two-stages PSO algorithm can provide one realistic optimal trajectory from any two cities in the complicated three-dimensional GIS scenario, such as Chang Sha city and Nan Chang city.

关键词： evolutionary computation geographic information systems particle swarm optimisation railway engineering Chang Sha city GIS system Nan Chang city altitude factor computer science construction cost distance factor intelligent railway transportation optimal 3D railway trajectory optimal trajectory particle swarm optimization algorithm railway trajectory planning problem smart railway trajectory planning three-dimensional GIS scenario two-stages PSO algorithm algorithm design and analysis Cities and towns Linear programming Planning Rail transportation Trajectory evolutionary algorithm Rail transportation algorithm design and analysis Railway engineering Cities and towns geographic information systems Construction costs particle swarm optimization trajectory Distance factor computer science linear programming trajectory planning railways

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Path planning for unmanned vehicle searching based on sensor deployment and travelling salesman problem

Path planning for unmanned vehicle searching based on sensor...

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2014中国制导、导航与控制学术会议

作者： Bin Di Rui Zhou Yu Zhang Jun Che Science and Technology on Aircraft Control Laboratory Beihang UniversityBeijing100191 China AVIC Automatic Flight Control Research InstituteXi''an 710065 China

ISBN: (纸本)9781479946983

The path planning problem for unmanned vehicle searching over the region of interest with obstacles is investigated in this *** path planning problem is decomposed into the sensor deployment problem and the travelling salesman problem based on the fundamental finding that the vehicle could search the region that covered by the sensor if the vehicle pass through the position where the sensor is *** positions of the sensors deployed to cover the region completely are obtained by solving the sensor deployment *** the positions of the deployed sensors as the waypoints that the vehicle has to travel through,the shortest path connecting all the waypoints can be obtained by solving the travelling salesman *** cooperatively coevolving particle swarm optimization algorithm with multiple update rules is newly developed for the sensor deployment *** simulation results demonstrate the feasibility of the proposed methods.

关键词： mobile robots particle swarm optimisation path planning remotely operated vehicles sensor placement travelling salesman problems particle swarm optimization algorithm sensor deployment travelling salesman problem unmanned vehicle optimization particle swarm optimization Path planning Robot sensing systems Search problems Vehicles

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Fuzzy Comprehensive Evaluation for Transmission Grid Planning Based on Combination Weight

Fuzzy Comprehensive Evaluation for Transmission Grid Plannin...

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2014 International Conference on Power and Energy(ICPE 2014)

作者： Z.H.LI Y Lin W.H.Chen D.H.You G.Wang M.L.Dong Q.R.Chen Y.Zou J.Hu Guangdong Power Grid Development Research Institute Co. Ltd State Key Laboratory of Advanced Electromagnetic Engineering and Technology of Huazhong University of Science and Technology

In order to remedy the defect that the determination of weight in fuzzy comprehensive evaluation is impacted by subjectivity, the combination weight is introduced. In this paper, the optimization Model combines the subjective weight and objective weight, which is solved by PSO, is built. At the same time, In order to give full consideration to the uncertain and fuzzy factors which involved in the grid planning, this paper combines the combination weight and fuzzy comprehensive evaluation to assess the grid-planning scheme. The calculation results of IEEE Garver-6 show that the proposed fuzzy comprehensive evaluation system is clear in theory and convenient to calculate, the combination weight is objective and reasonable as well as its results are scientific and intuitive.

关键词： grid planning scheme fuzzy comprehensive evaluation combination weight particle swarm optimization algorithm

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Integrated Design of Missile Guidance and Control:Active Disturbance Rejection Control Method

Integrated Design of Missile Guidance and Control:Active Dis...

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2014中国制导、导航与控制学术会议

作者： Maopeng Ran Yifen Chen Qing Wang Chaoyang Dong Aojia Ma School of Automation Science and Electrical Engineering Beihang UniversityBeijing100191 China The 8th Institute of Shanghai Academy of Spaceflight TechnologyShanghai201109 China School of Aeronautic Science and EngineeringBeihang UniversityBeijing100191 China

ISBN: (纸本)9781479946983

In this paper,we present an active disturbance rejection control (ADRC) based integrated guidance and control (IGC) *** IGC law is designed using the backstepping technique,and the particle swarm optimization (PSO) algorithm is utilized to find the optimal selection of the controller *** results show that,with the application of the proposed IGC law,small miss distance can be achieved,and the system is robust against target maneuver and system uncertainties.

关键词： active disturbance rejection control control nonlinearities missile guidance particle swarm optimisation uncertain systems ADRC IGC law design PSO algorithm active disturbance rejection control method backstepping technique controller parameter selection integrated design integrated guidance and control missile control particle swarm optimization algorithm system uncertainties Aerodynamics Backstepping Educational institutions History Missiles Observers Uncertainty guided missile guidance Robust control overall design system uncertainties missile control Control nonlinearities missiles particle swarm optimization Backstepping Educational institutions uncertain systems Aerodynamics Observers

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Combining CA and PSO to Solve Flexible Job shop Scheduling Problem

Combining CA and PSO to Solve Flexible Job shop Scheduling P...

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第26届中国控制与决策会议

作者： ZHOU Wei BU Yan-ping ZHOU Ye-qing School of Business East China University of Science and Technology School of Technology Shanghai Jiaotong University School of Mathematical sciences Fudan University

The flexible job shop scheduling problem(FJSP) is an extension of the classic job shop scheduling problem(JSP),which breaks through the uniqueness of limit resources,allows a procedure in many machines processing and one machine processing many kinds of different types of *** is more practical and complex than *** computational complexity of FJSP is much higher,which disables exact solution methods and makes heuristic approaches more qualified.A hybrid optimization algorithm,CPSO,based on the cultural algorithm and particle swarm optimization algorithm,is proposed in this paper to solve the *** objective is to minimize *** results show that this hybrid method is able to solve efficiently these kinds of problems.

关键词： cultural algorithm particle swarm optimization algorithm flexible job shop scheduling problem makespan

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Ground-state structure determination and mechanical properties of palladium seminitride

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Chinese Physics B 2013年第11期22卷 431-435页

作者：张刚台白婷婷赵亚儒卢成 Department of Physics and Information Technology Baoji University of Arts and Sciences Department of Mathematics Baoji University of Arts and Sciences Department of Physics Nanyang Normal University

Combining first-principles calculations with the particle swarm optimization （PSO） algorithm, we have explored the ground-state structure of Pd2N, whose structure is in debate although it is the first synthesized binary platinum group nitride. The ground-state structure is predicted to be tetragonal with space group P^-4m2, which is energetically more favorable than the previously proposed orthorhombic Co2N-type structure. The stability is confirmed by the subsequent calculations on the phonon dispersion curves and elastic constants. Furthermore, the calculated mechanical properties indicate that Pd2N has low incompressibility and is a common hard material.

关键词： Pd2N particle swarm optimization algorithm ground-state structure first-principles calculations

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Cultural algorithm Based on particle swarm optimization for Partner Selection of Virtual Enterprise

Cultural Algorithm Based on Particle Swarm Optimization for ...

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第三十一届中国控制会议

作者： ZHOU Wei~1,BU Yan-ping~2 1.School of Business,East China University of Science and Technology,Shanghai,200237,P.R.China 2.School of Technology,Shanghai Jiaotong University,Shanghai,201101,P.R.China

A novel cultural algorithm based on particle swarm optimization(PSO) algorithm was proposed in this *** analyzing the partner selection problems of virtual enterprise,the CPSO algorithm was presented to solve enterprise alliance problem within reasonable time and *** are certain number partners of each sub-task in virtual enterprise environment. The objective is,by selecting the optimal combination of partners,to minimize project's completion time and project's total cost. We tested the CPSO algorithm against the PSO *** results demonstrate that it can be superior to the regular PSO. We also tested the CPSO algorithm with the exhaustion method to show the algorithm's efficiency.

关键词： cultural algorithm particle swarm optimization algorithm virtual enterprise partner selection

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Based on region division setup planning for sheet metal assembly in aviation industry

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2013年第B1期227卷 153-161页

作者： Liu, Ping Li, Yuan Zhang, Kai-fu Cheng, Hui Northwestern Polytech Univ Minist Educ Key Lab Contemporary Design & Integrated Mfg Tech Xian 710072 Peoples R China

The task of setup planning is to determine the number and sequence of setups and the machining features or operations in each setup. Due to the locating method's character, the position of the locators may keep away joining tools from arriving at joining points during the joining process of sheet metal. In order to solve these problems, a novel method is proposed, which consists of three parts: (1) the points on the skin are divided into sets of supplied riveting and prior riveting points by convex hull generating method;(2) fixture vector is defined, and a suitable fixture region is generated by vector multiplication;and (3) the particle swarm optimization algorithm is applied to generate optimal setup scheme by setting up the parameters. The new setup planning approach can accomplish riveting work by minimum setup times and workpiece variation without finite element analysis software. At last, a certain kind of aircraft wing joining process is illustrated as an example to demonstrate the effect of the proposed approach.

关键词： Setup planning supplied riveting set particle swarm optimization algorithm sheet metal assembly

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STUDY ON CLOUD RESOURCE ALLOCATION STRATEGY BASED ON particle swarm ANT COLONY optimization algorithm

STUDY ON CLOUD RESOURCE ALLOCATION STRATEGY BASED ON PARTICL...

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2012 IEEE 2nd International Conference on Cloud Computing and Intelligence Systems

作者： Zhengqiu Yang Meiling Liu Jiapeng Xiu Chen Liu School Of Software Engineering Beijing University of Posts and Telecommunications School Of Computer Science Beijing University of Posts and Telecommunications

Cloud computing environment for the efficient resources allocation is an important issue in the field of cloud computing. The resources in Cloud computing application platform are distributed widely and with great diversity. User demands of real-time dynamic change are very difficult to predict accurately. The heuristic ant colony algorithm could be used to solve this kind of problems, but the algorithm has slow convergence speed and parameter selection problems. Aiming at this problem, this paper proposes an optimized ant colony algorithm based on particle swarm to solve cloud computing environment resources allocation problem.

关键词： particle swarm optimization algorithm Ant colony optimization algorithm Cloud resource allocation

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System reliability theory based multiple-objective optimization model for construction projects

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AUTOMATION IN CONSTRUCTION 2013年 31卷 54-64页

作者： Tao, Ran Tam, Chi-Ming City Univ Hong Kong Dept Civil & Architectural Engn Hong Kong Hong Kong Peoples R China

Although the system reliability theory has a high capability in quality quantification, while system reliability optimization (SRO) has been well developed in manufacturing engineering, seldom can their applications be found in the construction industry. This study aims to develop a system reliability theory based multiple-objective optimization model to conduct SRO, and then identify the cost-quality trade-off solution for construction projects. First the whole construction project is treated as a system composed of different work packages. Second, the reliability function is employed to quantify the quality performance and the nonlinear cost-reliability function is set up. Moreover, according to the physical arrangements among each work package, the system reliability structural function is determined. Third, the total construction cost minimization and system reliability maximization are defined as multiple-objectives. The particle swarm optimization algorithm is employed to search for the Pareto-optimal solutions, from which the final cost-quality trade-off solution can be selected. A real construction case is used to evaluate the workability of the proposed model and the results have fully proven its validity and practicality. (C) 2012 Elsevier B.V. All rights reserved.

关键词： System reliability theory System reliability optimization Multiple objectives optimization model particle swarm optimization algorithm

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