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Research on Optimization of Project Time-Cost-Quality Based on particle swarm Optimization

INTERNATIONAL JOURNAL OF INFORMATION SYSTEMS AND SUPPLY CHAIN MANAGEMENT 2019年第2期12卷 76-88页

作者： Song, Yanqing Hou, Genran Yellow River Conservancy Tech Inst Kaifeng Peoples R China

In order to make proper time-cost-quality decisions for projects, an improved particle swarm optimization algorithm is applied. First, the optimal model of project time-cost-quality is constructed considering all factors. Second, the basic theory of particle swarm algorithms is summarized, and the improved particle swarm algorithm is put forward based on vector principle, and then the rotational base technology is introduced into the improved particle swarm algorithm to construct a multiple objective optimization algorithm. Finally, the simulation analysis is carried out using a project as example, and the optimal parameters are obtained.

关键词： improved particle swarm algorithm Optimization particle swarm algorithm Time-Cost-Quality Optimization

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Optimization Scheduling of Power System Based on improved particle swarm Optimization

Optimization Scheduling of Power System Based on Improved Pa...

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International Conference on Power System Technology (POWERCON)

作者： Lu, Mengke Du, Wei Tian, Ruiping Li, Deyi Xi An Jiao Tong Univ Sch Elect Engn Xian 710049 Shaanxi Peoples R China

ISBN: (纸本)9781538664612

To address the issue of short-term scheduling of power system that contains clean energy, in this paper, an adaptive weighted immune particle swarm optimization algorithm is proposed to overcome the shortcomings of the traditional particle swarm optimization algorithm including easily falling into the local optimum value and slow convergence speed. The superiority of the improved algorithm is verified on the 10-unit test system. On the basis of the existing scheduling model, we propose the spinning reserve model to improve the ability of power system to cope with risks. In view of the complex inequalities and equality constraints in the scheduling model, this paper proposes an extra penalty point hybrid constraint plan based on elite retention. By introducing the elite retention mechanism, the quality of reservation and information transferring in the iterative process is improved, the quality of the particle swarm optimization algorithm is optimized and the optimization speed of the original hybrid constraint plan is effectively accelerated. Finally, the rationality and feasibility of the improved particle swarm algorithm and scheduling model are verified on the improved IEEE-RTS test system, which provides tactics and feasible suggestions for the economic dispatching operation of power systems with clean energy.

关键词： clean energy improved particle swarm algorithm spinning reserve model elite retention

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Modeling and Tracking Control of Unmanned Helicopter 6

Modeling and Tracking Control of Unmanned Helicopter

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IEEE 6th International Conference on Control Science and Systems Engineering (ICCSSE)

作者： Dai, Jiyang Nie, Hang Ying, Jin Zhao, Yukun Sun, Yijun Nanchang Hangkong Univ Sch Informat Engn Nanchang Jiangxi Peoples R China

ISBN: (纸本)9781728198460

In path tracking control systems of unmanned helicopters, it is essential to have high-performance controllers. Currently many design methods for tracking controllers have general control effects, but it is difficult to provide theoretical and practical basis for real unmanned helicopter tracking control systems. In this paper, the external PID controller of an unmanned helicopter is designed, in which an improved particle swarm optimization (PSO) algorithm is proposed to adjust controller parameters. To test and verify the performance of the controller, an improved artificial potential field (APF) method of tracking with combination of unmanned helicopter model. Firstly, safe distance of the unmanned helicopter is reserved in the course of flight. Secondly, the path planning produced by artificial potential field method is taken as the expected trajectory and the input into the unmanned helicopter model in real time to verify the control effect of the controller under complex and irregular conditions. Finally, the simulation is conducted to verify the feasibility of the proposed method and the rapidity of the responses. The simulation results show that the flight trajectory of the unmanned helicopter is basically consistent with the desired trajectory, and the tracking error trajectory has a high degree of convergence with reaching the target point smoothly,

关键词： unmanned helicopter PID controller improved particle swarm algorithm improved artificial potential field method

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Research on Urban Intelligent Transportation Planning Based on Storm 18

Research on Urban Intelligent Transportation Planning Based ...

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18th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)

作者： Liu, Chun-Yan Zou, Cheng-Ming Wuhan Univ Technol HuaXia Coll Dept Informat Engn Wuhan Hubei Peoples R China Hunan Vocat Coll Railway Technol Hunan Engn Technol Res Ctr Highspeed Railway Oper Zhuzhou Hunan Peoples R China

ISBN: (纸本)9781728128658

This paper aims at the problem of dynamic target path planning for vehicles in urban. Firstly,design an urban real-time dynamic path planning model(UR-MODE) based on the Storm framework;then, proposes an improved particle swarm optimization algorithm(Adaptive Partner-particle swarm optimization,AP-PSO) which introduce the adaptive inertia weight and small scale perturbation strategy to ensure the efficiency of our proposed model. Finally,we implement the improved algorithm on the Storm real-time processing system and realize the mass real-time traffic data processing. Compared with the existing path planning algorithms, the experiment proves that the UR-MODE based method can reduce the travel time by 15%-20% on average and increase the traffic resource utilization by 50%. It proves the efficiency of the method.

关键词： improved particle swarm algorithm multi-objective optimization optimal path storm frame

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Research on Temperature Control System Based on IPSO Optimized Fuzzy PID

Research on Temperature Control System Based on IPSO Optimiz...

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第三十九届中国控制会议

作者： Gao Long Yang Yi Ren Xiaolin Gu Haiqin Han Qingqing Yu Jingya College of Electrical Engineering Nantong University

The air source heat pump heat collection control system is a system with strong nonlinearity and large time *** is difficult to achieve the expected control effect on the outlet water temperature by using conventional control *** view of this problem,this paper makes up for the shortcomings of fuzzy control and PID control,and proposes an improved particle swarm optimization（IPSO） fuzzy PID control strategy to control the temperature of the effluent of the air source heat pump heat collection control *** of all,the particle swarm optimization algorithm is easy to fall into the problem of local *** increasing the vector,the optimal position of individual particles is stored to increase the range of particle *** use the improved particle swarm algorithm to optimize the fuzzy PID scale factor and quantization factor to modify the PID parameters in real time to improve the control accuracy of the heat pump ***,a MATLAB/Simulink simulation model for the heat collection temperature control of the heat pump is established and an improved particle swarm optimization algorithm program is written,and compared with the fuzzy PID and conventional PID control *** results show that the heat-collection control system based on IPSO has strong adaptability,robustness and anti-interference.

关键词： Air source heat pump fuzzy PID improved particle swarm algorithm temperature control

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Effective Form Error Assessment Using improved particle swarm Optimization

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MAPAN-JOURNAL OF METROLOGY SOCIETY OF INDIA 2017年第4期32卷 279-292页

作者： Pathak, V. K. Singh, A. K. Malaviya Natl Inst Technol Dept Mech Engn JLN Marg Jaipur 302017 Rajasthan India

This paper presents an improved variant of particle swarm optimization (MPSO) algorithm for the form error evaluation, from a set of coordinate measurement data points. In classical particle swarm optimization (PSO), new solution is updated by the existing one without really comparing which one is better. This behaviour is considered to be caused by lack in exploitation ability in the search space. The proposed algorithm generates new swarm position and fitness solution employing an improved and modified search equation. In this step, the swarm searches in proximity of the best solution of previous iteration to improve the exploitation behaviour. The particle swarm employs greedy selection procedure to choose the best candidate solution. A non-linear minimum zone objective function is formulated mathematically for each form error and consequently optimized using proposed MPSO algorithm. Five benchmark functions are used to prove the efficiency of the proposed MPSO algorithm, by comparing the proposed algorithm with established PSO and genetic algorithm. Finally, the results of the proposed MPSO algorithm are compared with previous literature and with other nature inspired algorithms on the same problem. The results validate that proposed MPSO algorithm is more efficient and accurate as compared to other conventional methods and is well suited for effective form error evaluation using CMMs.

关键词： Form error assessment improved particle swarm algorithm Minimum zone evaluation

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Energy storage device locating and sizing based on power electronic transformer

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JOURNAL OF ENGINEERING-JOE 2019年第16期2019卷 3164-3168页

作者： Geng, Qi Hu, Yan Shanghai Jiao Tong Univ Sch Elect & Informat Engn Shanghai Peoples R China

In this study, firstly, the bi-directional energy flow of grid-connected photovoltaic and energy storage system based on power electronic transformer is demonstrated. Based on this, a bi-level programming model is proposed for the location and capacity of energy storage. The optimisation of the location of the outer layer is based on the improved particle swarm algorithm. The energy storage location is a variable, and network loss as well as PET loss are objective functions. The improved particle swarm optimisation algorithm is still adopted to optimise the capacity in the inner layer. The cost of electricity from the main grid is taken as the objective function, and the economic dispatch is realised based on the energy routing strategy of power electronic transformer.

关键词： power grids particle swarm optimisation energy storage photovoltaic power systems power transformers power electronic transformer bi-directional energy flow grid-connected photovoltaic bi-level programming model improved particle swarm algorithm improved particle swarm optimisation algorithm energy routing strategy energy storage device

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Network reconfiguration and operation optimisation of distribution system with flexible DC device

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JOURNAL OF ENGINEERING-JOE 2019年第16期2019卷 2401-2404页

作者： Yang, Yanhong Zhang, Shizhong Pei, Wei Sun, Jianjun Lu, Yi Chinese Acad Sci Inst Elect Engn 6 Beiertiao Beijing 100190 Peoples R China Univ Chinese Acad Sci 19A Yuquan Rd Beijing 100049 Peoples R China Wuhan Univ Sch Elect Engn Wuhan 430072 Hubei Peoples R China State Grid Zhejiang Elect Power Res Inst Hangzhou 310014 Zhejiang Peoples R China

In this study, a comprehensive reconfiguration and operation optimisation of distribution system with flexible DC device (FDD) is discussed. The structures of distribution network with FDD are illustrated and the exact operating of FDD set-points are discussed which realised power flow control and loop closing operation of distribution system. Then, the non-linear combinational optimisation models of reconfiguration and operation optimisation with FDD are established considering different objectives and constraints. In order to solve the complicated optimisation problems, improved particle swarm algorithm and sequential AC/DC power flow method are used. Results show that the FDD have stronger ability to promote the performance of distribution system with power flow optimal adjusting and cooperation with traditional switch.

关键词： particle swarm optimisation load flow control power distribution control combinatorial mathematics operation optimisation distribution system flexible DC device comprehensive reconfiguration distribution network exact operating FDD set-points power flow control loop closing operation nonlinear combinational optimisation models complicated optimisation problems network reconfiguration sequential AC-DC power flow method improved particle swarm algorithm

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Research on Urban Intelligent Transportation Planning Based on Storm

Research on Urban Intelligent Transportation Planning Based ...

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第十八届分布式计算及其应用国际学术研讨会

作者： Chun-Yan LIU Cheng-Mmg ZOU Xiao-Hong QIAN Department of Information Engineering Wuhan University of Technology HuaXia College Hunan Engineering Technology Research Center for High-speed Railway Operation Safety Assurance(Hunan Vocational College of Railway Technology) School of Computer Science and Technology Wuhan University of Technology

This paper aims at the problem of dynamic target path planning for vehicles in ***,design an urban real-time dynamic path planning model(UR-MODE)based on the Storm framework;then,proposes an improved particle swarm optimization algorithm(Adaptive Partnerparticle swarm optimization,AP-PSO) which introduce the adaptive inertia weight and small scale perturbation strategy to ensure the efficiency of our proposed ***,we implement the improved algorithm on the Storm real-time processing system and realize the mass real-time traffic data *** with the existing path planning algorithms,the experiment proves that the UR-MODE based method can reduce the travel time by 15%-20% on average and increase the traffic resource utilization by 50%.It proves the efficiency of the method.

关键词： improved particle swarm algorithm multiobjective optimization optimal path storm frame

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Evolution Balancing of Mixed Model-Assembly Line Considering Task Reassignment

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JOURNAL OF ADVANCED MANUFACTURING SYSTEMS 2018年第1期17卷 3-21页

作者： Wu, Y. M. Dai, L. Zh. Luo, L. F. Guizhou Univ Key Lab Modern Mfg Technol Guiyang Guizhou Peoples R China

Market demand and technological progress drive continuous product evolution, upgrade and innovation, which necessitate readjustment and evolution balancing of the mixed model assembly line (MMAL) for improving production efficiency. In the evolution process of MMAL, the rational matching between difficulty of assembly tasks and operating level are mainly considered, the mathematical model of evolution balancing optimization for MMAL is established, and an improved particle swarm optimization algorithm (IPSO) based on leapfrog algorithm is designed. In the process of optimization, the single population is divided into several subgroups for searching, information exchange between species is executed to get better particles and the strategy of returning to the beginning is introduced, in which particle diversity and global search capability are increased and improved, respectively. Finally, the effectiveness and feasibility of the method were validated by evolution balancing planning of MMAL.

关键词： Mixed model assembly line task assignment evolution balancing improved particle swarm algorithm

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