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6th International Symposium on Parallel Architectures, algorithms, and Programming (PAAP)

作者： Wang, Yiyi Huang, Mei Zhang, Caiping Beijing Jiaotong Univ Natl Act Distribut Network Technol Res Ctr NANTEC Beijing Peoples R China

ISBN: (纸本)9781479938445

The electricity consumption of a charging station is obviously fluctuant. An energy storage system is an effective device to improve the load variation of the charging station. In order to give full play to energy storage system, this paper proposes a charging and discharging control strategy of the storage system considering multi-objective including stabilizing load and economizing electric consumption, changing a bi-objective problem into a single objective problem with weights after fuzzy processing. This paper solves the problem based on particle swarm optimization algorithm, and considers constraints including the demand of uninterruptible power supply and SOC at given time, which has greater practical significance. In this paper, the data of a practical integrated smart grid construction project is taken into considered. Finally, we verify the effectiveness and feasibility of the optimization strategy after simulation analysis.

关键词： energy storage system charging scheduling electric vehicles particle swarm optimization algorithm multi-objective optimization

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Identifying New Prognostic Features for Remaining Useful Life Prediction 16

Identifying New Prognostic Features for Remaining Useful Lif...

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16th International Power Electronics and Motion Control Conference and Exposition (PEMC)

作者： Boukra, Tahar Lebaroud, Abdesselam Univ 20 Aout 1955 Skikda Dept Genie Elect Skikda Algeria

ISBN: (纸本)9781479920600

An accurate prediction of the remaining useful life (RUL) from a prognosis system relies on a good selection of prognosis features. The latter should well capture the trend of the fault progression. In situation where the existence of the fault to failure data is rare and the development of degradation based model is difficult, we must be addressed to the identification of new features having the qualities mentioned above. This paper present an new selection method based upon the particle swarm optimization (PSO) algorithm to identify the advanced prognosis feature and the Hidden Semi Markov Model (HSMM) for the prediction of the remaining useful life. This method was validated on a set of experimental data collected from bearing failures.

关键词： Prognostics Remaining Useful Life particle swarm optimization algorithm Feature selection Hidden Semi Markov Model

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Bio Inspired Approach for Load Balancing to Reduce Energy Consumption in Cloud Data Center

Bio Inspired Approach for Load Balancing to Reduce Energy Co...

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International Conference on Communication, Control and Intelligent Systems

作者： Akhil Goyal Navdeep S. Chahal Software Technology Division C-DAC

ISBN: (纸本)9781467375429

Cloud computing has made it feasible to access various IT resources through a high speed network from anywhere in the world. Constant increasing demand of cloud computing is equally popular in consumers as well as providers. But along with advancement every technology is also associated with some ill effects. On same path, cloud computing also accompanies a serious issue with it and that issue is energy consumption. In this paper Firefly algorithm has been selected as a proposed bio-inspired approach to perform load balancing to reduce energy consumption in cloud data center. Further, the results are compared with particle swarm optimization algorithm (PSO). The energy consumed in case of Firefly algorithm is less than energy consumed in PSO algorithm.

关键词： Cloud Computing Firefly algorithm particle swarm optimization algorithm Energy Consumption Load Balancing

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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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2nd IEEE International Conference on Cloud Computing and Intelligent Systems (CCIS)

作者： Yang, Zhengqiu Liu, Meiling Xiu, Jiapeng Liu, Chen Beijing Univ Posts & Telecommun Sch Software Engn Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Sch Comp Sci Beijing 100876 Peoples R China

ISBN: (纸本)9781467318556;9781467318570

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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Nonlinear Inertia Weigh particle swarm optimization combines Simulated Annealing algorithm and Application in Function and SVM optimization

Nonlinear Inertia Weigh Particle Swarm Optimization combines...

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3rd International Conference on Mechanical and Electronics Engineering (ICMEE 2011)

作者： Jiao Bin Xu Zhixiang Shanghai DianJi Univ Sch Elect Engn Shanghai 200240 Peoples R China

ISBN: (纸本)9783037852866

This paper proposes an improved particle swarm optimization algorithm (PSO) for the global and local equilibrium problem of searching ability. It improves the iterative way of inertia weight in PSO, using non-linear decreasing algorithm to balance, then PSO combines with simulated annealing(SA). Finally, the optimization test experiments are carried out for the typical functions with the algorithm (ULWPSO-SA), and compare with the basic PSO algorithm. Simulation experiments show that local search ability of algorithm, convergence speed, stability and accuracy have been significantly improved. In addition, the novel algorithm is used in the parameter optimization of support vector machines (ULWPSOSA-SVM), and the experimental results indicate that it gets a better classification performance compared with SVM and PSO-SVM.

关键词： particle swarm optimization algorithm inertia weight simulated annealing algorithm function optimization parameter optimization

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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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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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