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Optimal Design for a Retrial Queueing System with State-Dependent Service Rate

Journal of Systems Science & Complexity 2017年第4期30卷 883-900页

作者： ZHANG Xuelu WANG Jinting MA Qing Department of Mathematics Beijing Jiaotong University School of Science Qilu University of Technology Department of Basic Courses Shandong Women’s University

This paper considers a single server retrial queue in which a state-dependent service policy is adopted to control the service rate. Customers arrive in the system according to a Poisson process and the service times and inter-retrial times are all exponentially distributed. If the number of customers in orbit is equal to or less than a certain threshold, the service rate is set in a low value and it also can be switched to a high value once this number exceeds the threshold. The stationary distribution and two performance measures are obtained through the partial generating functions. It is shown that this state-dependent service policy degenerates into a classic retrial queueing system without control policy under some conditions. In order to achieve the social optimal strategies, a new reward-cost function is established and the global numerical solutions, obtained by Canonical particle swarm optimization algorithm, demonstrate that the managers can get more benefits if applying this state-dependent service policy compared with the classic model.

关键词： particle swarm optimization algorithm retrial queues social optimal strategy statedependent service policy

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Integration of particle swarm optimization and Immune Genetic algorithm-Based Dynamic Clustering for Customer Clustering

Integration of Particle Swarm Optimization and Immune Geneti...

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8th Panhellenic Conference on Artificial Intelligence

作者： Kuo, R. J. Lin, S. H. Chen, Zhen-Yao Natl Taiwan Univ Sci & Technol Dept Ind Management Taipei 10607 Taiwan Taiwan Semicond Mfg Co Hsinchu 30078 Taiwan De Lin Inst Technol Dept Business Adm New Taipei City 23654 Taiwan

This study intends to present a dynamic clustering (DC) approach based on particle swarm optimization (PSO) and immune genetic (IG) (DCPIG) algorithm, which is able to cluster the data into adequate clusters through data characteristics with pre-specified numbers of clusters. The proposed DCPIG algorithm is compared with three DC algorithms in the literature using Iris, Wine, Glass and Vowel benchmark data sets. The experiment results show that the DCPIG algorithm can achieve higher stability and accuracy than the other algorithms. In addition, the DCPIG algorithm is also applied to a real-world problem considering the customer clustering for a cyber flower shop. Lastly, we recommend different products and services to customers based on the clustering results.

关键词： Cluster analysis dynamic clustering particle swarm optimization algorithm immune genetic algorithm

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A machine learning technique toward generating minimum energy structures of small boron clusters

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INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY 2018年第17期118卷

作者： Mitikiri, Praveen Jana, Gourhari Sural, Shamik Chattaraj, Pratim K. Indian Inst Technol Kharagpur Dept Comp Sci & Engn Kharagpur 721302 W Bengal India Indian Inst Technol Kharagpur Dept Chem Kharagpur 721302 W Bengal India Indian Inst Technol Kharagpur Ctr Theoret Studies Kharagpur 721302 W Bengal India

The search for a global minimum related to molecular electronic structure and chemical bonding has received wide attention based on some theoretical calculations at various levels of theory. particle swarm optimization (PSO) algorithm and modified PSO have been used to predict the energetically stable/metastable states associated with a given chemical composition. Out of a variety of techniques such as genetic algorithm, basin hopping, simulated annealing. PSO, and so on. PSO is considered to be one of the most suitable methods due to its various advantages over others. We use a swarm-intelligence based parallel code to improve a PSO algorithm in a multidimensional search space augmented by quantum chemical calculations on gas phase structures at 0 K without any symmetry constraint to obtain an optimal solution. Our currently employed code is interfaced with Gaussian software for single point energy calculations. The code developed here is shown to be efficient. Small population size (small cluster) in the multidimensional space is actually good enough to get better results with low computational cost than the typical larger population. But for larger systems also the analysis is possible. One can try with a large number of particles as well. We have also analyzed how arbitrary and random structures and the local minimum energy structures gravitate toward the target global minimum structure. At the same time, we compare our results with that obtained from other evolutionary techniques.

关键词： B-5 and B-6 clusters global optimal structure multi-threaded code optimization particle swarm optimization algorithm

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Integrated parallel forecasting model based on modified fuzzy time series and SVM

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Journal of Systems Engineering and Electronics 2017年第4期28卷 766-775页

作者： Yong Shuai Tailiang Song Jianping Wang Technical Support Engineering Faculty Academy of Armored Forced Engineering Beijing 100072 China Unit 68207 of the PLA Jiayuguan 735100 China China Defense Science & Technology Information Center Beijing 100142 China

A dynamic parallel forecasting model is proposed, which is based on the problem of current forecasting models and their combined model. According to the process of the model, the fuzzy C-means clustering algorithm is improved in outliers operation and distance in the clusters and among the clusters. Firstly, the input data sets are optimized and their coherence is ensured, the region scale algorithm is modified and non-isometric multi scale region fuzzy time series model is built. At the same time, the particle swarm optimization algorithm about the particle speed, location and inertia weight value is improved, this method is used to optimize the parameters of support vector machine, construct the combined forecast model, build the dynamic parallel forecast model, and calculate the dynamic weight values and regard the product of the weight value and forecast value to be the final forecast values. At last, the example shows the improved forecast model is effective and accurate.

关键词： fuzzy C-means clustering fuzzy time series interval partitioning support vector machine particle swarm optimization algorithm parallel forecasting

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Fast basis search for adaptive Fourier decomposition

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EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2018年第1期2018卷 74-74页

作者： Wang, Ze Wan, Feng Wong, Chi Man Qian, Tao Univ Macau Dept Elect & Comp Engn Fac Sci & Technol Ave Univ Taipa Macau Peoples R China Univ Macau Dept Math Fac Sci & Technol Ave Univ Taipa Macau Peoples R China

The adaptive Fourier decomposition (AFD) uses an adaptive basis instead of a fixed basis in the rational analytic function and thus achieves a fast energy convergence rate. At each decomposition level, an important step is to determine a new basis element from a dictionary to maximize the extracted energy. The existing basis searching method, however, is only the exhaustive searching method that is rather inefficient. This paper proposes four methods to accelerate the AFD algorithm based on four typical optimization techniques including the unscented Kalman filter (UKF) method, the Nelder-Mead (NM) algorithm, the genetic algorithm (GA), and the particle swarm optimization (PSO) algorithm. In the simulation of decomposing four representative signals and real ECG signals, compared with the existing exhaustive search method, the proposed schemes can achieve much higher computation speed with a fast energy convergence, that is, in particular, to make the AFD possible for real-time applications.

关键词： Adaptive Fourier decomposition Unscented Kalman filter Nelder-Mead algorithm Genetic algorithm particle swarm optimization algorithm

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The State of Charge Estimation of Power Lithium Battery Based on RBF Neural Network Optimized by particle swarm optimization

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JOURNAL OF APPLIED SCIENCE AND ENGINEERING 2017年第4期20卷 483-490页

作者： Hou, Zhixiang Xie, Ping Hou, Jiqiang Changsha Univ Sci & Technol Coll Automobile & Mech Engn Changsha 410004 Hunan Peoples R China

In order to ensure the safe and stable operation of electric vehicles (EV), it is necessary to accurately estimate the state of charge (SOC) of power lithium battery for electric vehicle. Because of the nonlinear relationship between SOC and its influencing factors, RBF neural network has obvious advantages in solving nonlinear problems, so in this paper, an SOC estimation method of power battery based on RBF neural network is proposed. In order to improve the accuracy of SOC estimation, we use particle swarm optimization (PSO) to optimize the RBF neural network model and identify the value of RBF network center vector and the weights through global optimal searching ability of PSO algorithm. The results simulation show that the SOC model based on PSO-RBF neural network has good estimation accuracy.

关键词： Electric Vehicle State-of-charge Estimation Radial Basis Function Neural Network particle swarm optimization algorithm

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A joint indoor positioning scheme exploiting pedestrian dead reckoning and radio frequency tomography

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JOURNAL OF ELECTROMAGNETIC WAVES AND APPLICATIONS 2018年第18期32卷 2386-2403页

作者： Zhou, Biao Jing, Changqiang Sun, Chao Kim, Youngok Jiangnan Univ Sch Internet Things Engn Wuxi Peoples R China Linyi Univ Sch Informat Linyi Peoples R China Kwangwoon Univ Dept Elect Engn Seoul South Korea

The pedestrian dead reckoning (PDR) is a popular technique for recognizing a pedestrian's trajectory. However, its inherent disadvantage of susceptibility to error accumulation limits its performance as the travelled distance is increased. Various techniques are combined with PDR to mitigate the error accumulation by regular position resetting. The radio frequency (RF) Tomography is an emerging technology for localization by sensing the received signal strength (RSS) fluctuation on a line-of-sight wireless link (LOSL). Conventional RF Tomography based schemes need specific large-scale networks and face with the problem of harsh environments and high hardware costs. Thus in this paper, we proposed the flexible and practical GF operators for PDR resetting work, in which we introduce the displacement estimation by PDR technique and the LOSL triggering sequence to build a PDR geometrical formulation (PDR-GF) model, and then this model is optimized by particle swarm optimization (PSO) algorithm for crossing point (CP) estimation on the LOSLs. Finally, the estimated CP is in turn used for regular position resetting. Practical experiments are performed to evaluate the proposed positioning scheme. According to the results, the proposed scheme can achieve a finer accuracy of less than 1 m, outperforming the existing RSS-PDR positioning scheme.

关键词： Pedestrian dead reckoning radio frequency tomography geometrical formulation position resetting particle swarm optimization algorithm

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Energy-Storage optimization Strategy for Reducing Wind Power Fluctuation via Markov Prediction and PSO Method

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ENERGIES 2018年第12期11卷

作者： Yun, Pingping Ren, Yongfeng Xue, Yu Inner Mongolia Univ Technol Coll Energy & Power Engn Hohhot 010051 Inner Mongolia Peoples R China Inner Mongolia Engn Res Ctr Wind Power Technol & Hohhot 010051 Inner Mongolia Peoples R China

Wind power penetration ratios of power grids have increased in recent years;thus, deteriorating power grid stability caused by wind power fluctuation has caused widespread concern. At present, configuring an energy storage system with corresponding capacity at the grid connection point of a large-scale wind farm is an effective solution that improves wind power dispatchability, suppresses potential fluctuations, and reduces power grid operation risks. Based on the traditional energy-storage battery dispatching scheme, in this study, a multi-objective hybrid optimization model for joint wind-farm and energy-storage operation is designed. The impact of two new aspects, the energy-storage battery output and wind-power future output, on the current energy storage operation are considered. Wind-power future output assessment is performed using a wind-power-based Markov prediction model. The particle swarm optimization algorithm is used to optimize the wind-storage grid-connected power in real time, to develop an optimal operation strategy for an energy storage battery. Simulations incorporating typical daily wind power data from a several-hundred-megawatt wind farm and rolling optimization of the energy storage output reveal that the proposed method can reduce the grid-connected wind power fluctuation, the probability of overcharge and over-discharge of the stored energy, and the energy storage dead time. For the same smoothing performance, the proposed method can reduce the energy storage capacity and improve the economic efficiency of the wind-storage joint operation.

关键词： wind-power fluctuation smoothing energy storage system Markov prediction model particle swarm optimization algorithm multi-objective optimization energy-storage battery output level

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Enhanced Bat algorithm for Analyzing the Reliability and Contingency Issue in Radial Distribution Power System

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2018年第19-20期46卷 2071-2083页

作者： Kumar, V. Swapnil, Shubham Ranjan, R. Singh, V. R. Maharshi Dayanand Univ Dept Elect Engn Rohtak Haryana India Univ Florida Dept Comp & Informat Sci & Engn Gainesville FL 32611 USA PDM Coll Engn Bhadurgarh India MDU Dept Elect Engn PDMCE Bahadurgarh Haryana India

The paper proposes the hybrid technique to observe the contingency and improve the reliability of radial distribution system (RDS). The hybrid algorithm used is interface of Bat algorithm and particle swarm optimization that is used to examine the contingency and reliability indices in the RDS. The feeder power loss, system node voltage deviation, system average interruption frequency index, system average interruption unavailability index and the energy not supplied at every load position in a sharing scheme are used to resolve the contingencies to minimize the power quality and reliability indices. The proposed techniques are implemented on MATLAB/Simulink platform and experienced on the IEEE 33 bus RDS. To establish the effectiveness of the proposed algorithm, results are compared with the reported Bat and Firefly algorithm.

关键词： IEEE 33 bus radial distribution system reliability contingency system node voltage deviation system average interruption frequency index system average interruption unavailability index enhanced bat algorithm particle swarm optimization algorithm power loss

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SSI effects on seismic behavior of smart base-isolated structures

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GEOMECHANICS AND ENGINEERING 2018年第2期14卷 161-174页

作者： Shourestani, Saeed Soltani, Fazlollah Ghasemi, Mojtaba Etedali, Sadegh Grad Univ Adv Technol Dept Civil & Surveying Engn Kerman Iran Birjand Univ Technol Dept Civil Engn Birjand Iran

The present study investigates the soil-structure interaction (SST) effects on the seismic performance of smart base-isolated structures. The adopted control algorithm for tuning the control force plays a key role in successful implementation of such structures;however, in most studied carried out in the literature, these algorithms are designed without considering the SSI effect. Considering the SSI effects, a linear quadratic regulator (LQR) controller is employed to seismic control of a smart base-isolated structure. A particle swarm optimization (PSO) algorithm is used to tune the gain matrix of the controller in both cases without and with SSI effects. In order to conduct a parametric study, three types of soil, three well-known earthquakes and a vast range of period of the superstructure are considered for assessment the SSI effects on seismic control process of the smart-base isolated structure. The adopted controller is able to make a significant reduction in base displacement. However, any attempt to decrease the maximum base displacement results in slight increasing in superstructure accelerations. The maximum and RMS base displacements of the smart base-isolated structures in the case of considering SSI effects are more than the corresponding responses in the case of ignoring SSI effects. Overall, it is also observed that the maximum and RMS base displacements of the structure are increased by increasing the natural period of the superstructure. Furthermore, it can be concluded that the maximum and RMS superstructure accelerations are significant influenced by the frequency content of earthquake excitations and the natural frequency of the superstructure. The results show that the design of the controller is very influenced by the SSI effects. In addition, the simulation results demonstrate that the ignoring the SSI effect provides an unfavorable control system, which may lead to decline in the seismic performance of the smart-base isolated structure incl

关键词： base isolation soil-structure interaction smart base-isolated structures LQR controller particle swarm optimization algorithm

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