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31st Chinese Control Conference

作者： Zhou Wei Bu Yan-ping East China Univ Sci & Technol Sch Business Shanghai 200237 Peoples R China Shanghai Jiao Tong Univ Sch Technol Shanghai 201101 Peoples R China

ISBN: (纸本)9789881563811

A novel cultural algorithm based on particle swarm optimization (PSO) algorithm was proposed in this paper. After analyzing the partner selection problems of virtual enterprise, the CPSO algorithm was presented to solve enterprise alliance problem within reasonable time and cost. There 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 method. Simulation 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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Drilling path optimization by the particle swarm optimization algorithm with global convergence characteristics

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2008年第8期46卷 2299-2311页

作者： Zhu, Guang-Yu Zhang, Wei-Bo Fuzhou Univ Sch Mech Engn & Automat Fuzhou 350002 Peoples R China

Drilling path optimization is one of the key problems in holes-machining. This paper presents a new approach to solve the drilling path optimization problem belonging to discrete space, based on the particle swarm optimization (PSO) algorithm. Since the standard PSO algorithm is not guaranteed to be global convergent or local convergent, based on the mathematical model, the algorithm is improved by adopting the method to generate the stop evolution particle once again to obtain the ability of convergence on the global optimization solution. Also, the operators are proposed by establishing the Order Exchange Unit (OEU) and the Order Exchange List (OEL) to satisfy the need of integer coding in drilling path optimization. The experimentations indicate that the improved algorithm has the characteristics of easy realization, fast convergence speed, and better global convergence capability. Hence the new PSO can play a role in solving the problem of drilling path optimization.

关键词： particle swarm optimization algorithm drilling path optimization global convergence PSO

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PSO-TPS: An Optimal Trust Path Selection algorithm Based on particle swarm optimization in Small World Network

PSO-TPS: An Optimal Trust Path Selection Algorithm Based on ...

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2nd International Conference on Cloud and Green Computing / 2nd International Conference on Social Computing and its Applications (CGC/SCA)

作者： Xu, Guangquan Xu, Chao Tian, Xiuming Zhang, Luxia Li, Xiaohong Li, Weisheng Tianjin Univ Sch Comp Sci & Technol Tianjin 300072 Peoples R China Chongqing Univ Posts & Telecommun Coll Comp Sci & Technol Chongqing Peoples R China

ISBN: (纸本)9780769548647;9781467330275

Small world network is a type of mathematical graph, in which most nodes are not neighbors of one another, but can be reached from every other by a small number of hops. P2P network is a typical small world network, it has a large scale and high risks, so trust model building and Trust Path Selecting (TPS) become big challenges. To solve TPS problem, we first reduce the scale of the complex P2P network by identifying and deleting the equivalent nodes. Then we provide a Trust Path Selection algorithm based on particle swarm optimization (PSO). In the algorithm, after initializing the particle swarm, each particle can update the speed and location according to its information, and then produce a new particle with better value. Repeating that process continually to implement the global search of the space, we can get the better trust path in the networks. The experimental results show that this algorithm is effective and efficient in finding the suboptimal solution of trust path, hence it can be applied in trust path searching in such small-world networks as P2P network.

关键词： Small World Network P2P Network Super Node particle swarm optimization algorithm

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Analysis of uniform circular arrays for adaptive beamforming applications using particle swarm optimization algorithm

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INTERNATIONAL JOURNAL OF RF AND MICROWAVE COMPUTER-AIDED ENGINEERING 2008年第1期18卷 42-52页

作者： Mahmoud, Korany R. Eladawy, Mohamed I. Bansal, Rajeev Zainud-Deen, Saber H. Ibrahem, Sabry M. M. Helwan Univ Dept Elect Commun & Comp Fac Engn Cairo Egypt Univ Connecticut Dept Elect & Comp Engn Storrs CT 06269 USA Menoufia Univ Dept Elect & Elect Commun Engn Fac Elect Engn Menoufia Egypt

In this article, the particle swarm optimization algorithm is used to calculate the complex excitations, amplitudes and phases, of the adaptive circular array elements. To illustrate the performance of this method for steering a signal in the desired direction and imposing nulls in the direction of interfering signals by controlling the complex excitation of each array element, two types of arrays are considered. A uniform circular array (UCA) and a planar uniform circular array (PUCA) with 16 elements of half-wave dipoles are examined. Also, the performance of an adaptive array using 3-bit amplitude and 4-bit phase shifters are studied. In our analysis, the method of moments is used to estimate the response of the dipole UCAs in a mutual coupling environment.

关键词： smart antennas adaptive beamforming method of moments mutual coupling uniform circular arrays particle swarm optimization algorithm adaptive array

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optimization of Waste Paper's Enzymatic Deinking Processes Based on Neural Network and particle swarm optimization

Optimization of Waste Paper's Enzymatic Deinking Processes B...

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International Conference on Mechatronic Sciences, Electric Engineering and Computer (MEC)

作者： Wu, Xinsheng Guangzhou Coll Guangdong Inst Sci & Technol Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781479925650

The optimal technological parameters of a waster paper's enzymatic deinking process with strong coupling, nonlinear and large time delay are difficult to achieve. BP neural network and improved particle swarm optimization (PSO) were applied to optimize enzymatic deinking process of waste paper. The theory and process were described. Enzymes dosage, temperature and pH were used as inputs of the network, a BP neural network model of the effective residual ink concentration (ERIC) and brightness of the pulp was established. The model had higher prediction precision compared with traditional regression model. The PSO was used to obtain the optimal conditions of deinking process with the lowest ERIC and highest brightness of the pulp. Experiments' results proved the method was an excellent tool for optimization of enzymatic deinking process.

关键词： BP neural network particle swarm optimization algorithm optimization enzymatic deinking

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Micro-grid Dynamic Modeling Based on RBF Artificial Neural Network

Micro-grid Dynamic Modeling Based on RBF Artificial Neural N...

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International Conference on Power System Technology (PowerCon)

作者： Cai Changchun Wu Min Deng Lihua Deng Zhixiang Zhang Jianyong Hohai Univ Changzhou Jiangsu Peoples R China Hohai Univ Coll IOT Engn Changzhou Jiangsu Peoples R China Hohai Univ Dept Mathes & Phys Engn Changzhou Jiangsu Peoples R China

ISBN: (纸本)9781479950324

A simplified equivalent model of microgrid, based on the RBF Artificial Neural Network, is present in this paper. The proposed model is suitable for the dynamic studies of microgrids. Nonlinear mapping of RBF neural network describes the dynamic characteristics of the Point of Common Couple(PCC) of micro-grid under the connected mode. The development model is evaluated using the voltage, current and power of the PCC as the input and output of the RBF neural network in the train process. The PSO algorithm is used for the parameter optimization of RBF and improved the generalization of the dynamic model. The simulation results show the proposed modeling method in this paper is suitable and effective, and the RBF neural network based dynamic model can describe the dynamic characteristics of micro-grid accurately.

关键词： RBF neural network Equivalent Modeling particle swarm optimization algorithm Micro-grid

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Active disturbance rejection control for output force creep characteristics of ionic polymer metal composites

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SMART MATERIALS AND STRUCTURES 2014年第7期23卷

作者： Xiong, Yan Chen, Yang Sun, Zhiyong Hao, Lina Dong, Jie Northeastern Univ Sch Mech Engn & Automat Shenyang Liaoning Peoples R China

Ionic polymer metal composites (IPMCs) are a type of electroactive polymer (EAP) that can be used as both sensors and actuators. An IPMC has enormous potential application in the field of biomimetic robotics, medical devices, and so on. However, an IPMC actuator has a great number of disadvantages, such as creep and time-variation, making it vulnerable to external disturbances. In addition, the complex actuation mechanism makes it difficult to model and the demand of the control algorithm is laborious to implement. In this paper, we obtain a creep model of the IPMC by means of model identification based on the method of creep operator linear superposition. Although the mathematical model is not approximate to the IPMC accurate model, it is accurate enough to be used in MATLAB to prove the control algorithm. A controller based on the active disturbance rejection control (ADRC) method is designed to solve the drawbacks previously given. Because the ADRC controller is separate from the mathematical model of the controlled plant, the control algorithm has the ability to complete disturbance estimation and compensation. Some factors, such as all external disturbances, uncertainty factors, the inaccuracy of the identification model and different kinds of IPMCs, have little effect on controlling the output block force of the IPMC. Furthermore, we use the particle swarm optimization algorithm to adjust ADRC parameters so that the IPMC actuator can approach the desired block force with unknown external disturbances. Simulations and experimental examples validate the effectiveness of the ADRC controller.

关键词： IPMC force creep model active disturbance rejection control particle swarm optimization algorithm

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Reactive power optimization based on improved particle swarm optimization algorithm with boundary restriction

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3rd International Conference on Electric Utility Deregulation, Restructuring and Power Technologies

作者： Liu, Hong Ge, Shaoyun Tianjin Univ Sch Elect Automat Engn Tianjin 300072 Peoples R China

ISBN: (纸本)9787564112493

At present, the particle swarm optimization algorithm is not effective in dealing with discrete variables, avoiding local optimization, and satisfying all inequality constraints of voltage and power factor. Therefore, this paper employs variable reflection and integralization to find the discrete correspondent of continuous variable in the particle;and introduces chaos strategy to the searching process to strengthen the capability of finding global optimization solution. Then this paper improves the optimization solution by adjusting the voltage and power factor that exceed limits with "nine palaces" strategy, to ensure the particles in feasible solution space. At last, this paper tests the proposed algorithm with the standard IEEE sample system and some actual network planning. The comparison of calculation results with those in other literatures proves that the new algorithm has more advantages at searching speed and quality.

关键词： electric power system reactive power control voltage regulation particle swarm optimization algorithm

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Study on the application of particle swarm optimization algorithm to power regulation of CPS in interconnect power grids

Study on the application of particle swarm optimization algo...

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IEEE Electrical Power and Energy Conference

作者： Liu Bin Wang Keying Zou Qing South China Univ Technol Coll Elect Power Guangzhou 510641 Peoples R China Guangdong Med Coll Sch Pub Hlth Dept Biomed Engn Guangzhou 523808 Peoples R China

ISBN: (纸本)9781424428946

The proportion of different AGC unit under the shortfall of power in the grid is investigated. The paper adopt PSO algorithm to optimize units of AGC (automatic generation control) which regulate the distribution of power and be in CPS controlling strategy. The optimal solution is hard to obtain by using average distribution but can be worked by using standard PSO algorithm, and then the CPS level can achieve the optimal under economic conditions. The paper proved the effectiveness of the algorithm by computing example simulation.

关键词： particle swarm optimization algorithm control performance standard automatic generation control regulation power

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Cellular particle swarm optimization algorithm for Logistics Distribution Locating Problem

Cellular Particle Swarm Optimization Algorithm for Logistics...

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International Seminar on Future BioMedical Information Engineering

作者： Liu, Yi Hangzhou Dianzi Univ Inst Management Sci & Informat Engn Hangzhou 310018 Zhejiang Peoples R China

ISBN: (纸本)9780769535616

Logistics distribution locating problem is an important area in Logistics, which select the most reasonable location of distribution centers from many places. This paper establish the Cellular PSO algorithm, which combine the particle swarm optimization algorithm and cellular automata. This algorithm was tested in the simulation experiment, and the result indicate that the Cellular PSO algorithm is a effective method of solving the problem of choosing the distribution centers location which can overcome the low precision of the basic particle swarm optimization algorithm. In additional, the Cellular PSO algorithm has high quality and efficiency of searching.

关键词： Cellular Automata particle swarm optimization algorithm Logistics Distribution Centers Location

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