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Task scheduling in a cloud computing environment using HGPSO algorithm

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第1-Sup期22卷 2179-2185页

作者： Kumar, A. M. Senthil Venkatesan, M. Tejaa Shakthi Inst Technol Women Dept CSE Coimbatore Tamil Nadu India KSR Inst Engn & Technol Tiruchengode Tamil Nadu India

Cloud computing delivers computing resources like software and hardware as a service to the users through a network. The main idea of cloud computing is to share the tremendous power of storage, computation and information to the scientific applications. In cloud computing, the user tasks are organized and executed with suitable resources to deliver the services effectively. There are plenty of task allocation techniques that are used to accomplish task scheduling. In order to enhance the task scheduling technique, an efficient task scheduling algorithm is proposed in this paper. optimization techniques are very popular in solving NP-hard problems. In this proposed technique, user tasks are stored in the queue manager. The priority is calculated and suitable resources are allocated for the task if it is a repeated task. New tasks are analyzed and stored in the on-demand queue. The output of the on-demand queue is given to the Hybrid Genetic-particle swarm optimization (HGPSO) algorithm. To implement HGPSO technique, genetic algorithm and particle swarm optimization algorithm are combined and used. HGPSO algorithm evaluates suitable resources for the user tasks which are in the on-demand queue.

关键词： Cloud computing optimization techniques Task scheduling Genetic algorithm particle swarm optimization algorithm

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A novel blind deconvolution method and its application to fault identification

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JOURNAL OF SOUND AND VIBRATION 2019年 460卷 114900-000页

作者： Cheng, Yao Chen, Bingyan Mei, Guiming Wang, Zhiwei Zhang, Weihua Southwest Jiaotong Univ State Key Lab Tract Power 111First SectNorth Second Ring Rd Chengdu 610031 Sichuan Peoples R China

Blind deconvolution is a method for enhancing the fault feature of rolling element bearings. Based on different maximization criteria, including kurtosis, correlated kurtosis, D-norm, multi-D-norm, and cyclostationarity indicator, different blind deconvolution algorithms have been proposed as powerful tools for fault feature extraction. However, kurtosis and D-norm are susceptible to extreme values, while the other three criteria strongly rely on prior knowledge of the fault period. To overcome the shortcomings of the existing criteria, this study proposes a new criterion called impulse-norm. It is a time-domain parameter defined as the ratio of the average amplitude of the first several maximum energy points to the energy of the entire signal. As opposed to kurtosis and D-norm, the impulse-norm is not affected by strong random impulses. Unlike correlation kurtosis, multi-D-norm and cyclostationarity indicator, it is also independent from the fault period. Based on impulse-norm, we also propose a new deconvolution algorithm called particle swarm optimization-based maximum impulse-norm deconvolution. This blind deconvolution algorithm employs generalized sphere coordinate transformation and adopts the PSO algorithm to optimally solve the filter coefficients by maximizing the impulse-norm of the signal being filtered. The proposed method was validated using simulated signals and high-speed train axle-box bearing experimental signals. The simulation and experimental results indicated that the proposed PSO-MIND method can effectively identify the weak impulse fault feature of rolling element bearings. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Blind deconvolution particle swarm optimization algorithm Fault identification Railway Rolling element bearing

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Thermodynamic, economic analysis and optimization of a heat pump driven desalination system with open-air humidification dehumidification configurations

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ENERGY 2019年 174卷 768-778页

作者： He, W. F. Chen, J. J. Zhen, M. R. Han, D. Nanjing Univ Aeronaut & Astronaut Coll Energy & Power Engn ECRG Nanjing 210016 Jiangsu Peoples R China

In this paper, the heat pump is coupled to a humidification dehumidification desalination system, with open-air configurations, to enhance the energy conversion efficiency. After establishing the energetic and entropic equations for all the thermal processes, the correlations between the desalination performance and the critical parameters, including the compression pressure ratio, pinch temperature difference of the condenser, terminal temperature difference of the evaporator, are revealed. Afterwards, the mass flow rate ratio and effectiveness during humidification and dehumidification are treated as the decision variables to optimize the energy conversion efficiency of the heat pump driven desalination system. The simulation results show that the actual top water production and gained-output-ratio of the desalination system reach 88.34 kgh(-1) and 3.72 at the balance condition of the humidifier. It is also obtained that raising the compression pressure ratio and reducing the pinch temperature difference of the condenser and terminal temperature difference of the evaporator, can promote the desalination performance. Furthermore, based on the particle swarm optimization algorithm, the best desalination performance, with 151.03 kgh(-1) for the water production, and 5.95 for the gained-output-ratio, is optimized within the prescribed range of the decision variables, while the corresponding cost of produced water arrives at 0.015$L-1 through the economic analysis. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Heat pump Humidification dehumidification desalination system Energy conversion efficiency Critical parameters particle swarm optimization algorithm

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Vessel traffic flow analysis and prediction by an improved PSO-BP mechanism based on AIS data

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EVOLVING SYSTEMS 2019年第3期10卷 397-407页

作者： Zhang, Ze-guo Yin, Jian-chuan Wang, Ni-ni Hui, Zi-gang Dalian Maritime Univ Nav Coll Dalian 116026 Peoples R China Chinese Acad Sci Inst Geog Sci & Nat Resources Res Beijing 100101 Peoples R China Dalian Maritime Univ Dept Math Dalian 116026 Peoples R China Qingdao Ocean Shipping Mariners Coll Dept Nav Qingdao 266071 Shandong Peoples R China

Vessel traffic flow forecasting is indispensable for the development of national shipping industry and the coordinated development of regional economy. In this paper, an improved PSO-BP (particle swarm optimization-back propagation) prediction model is established for the prediction of the total vessel traffic flow in a designated port area. The presented prediction model is referred to as SAPSO-BP neural network which utilizes the SAPSO (self-adaptive particle swarm optimization) algorithm to adjust the structure parameters of BP neural network. Facilitated by the establishment of foundation networks and satellite communication of Automatic Identification System (AIS) receivers, the detailed information of vessel is becoming increasingly obtainable. Therefore, a large number of real-observed vessel traffic flow data based on AIS records of Port area of Los Angeles (LA) has been chosen as the testing database to validate the effectiveness of the SAPSO-BP prediction model in vessel traffic flow forecasting. The grey correlation analysis (GCA) is employed to confirm the input dimension of the prediction model. Finally, simulation results demonstrate that the presented prediction approach can achieve vessel traffic flow trend predictions with reasonable, satisfactory convergence and stability.

关键词： AIS information particle swarm optimization algorithm BP neural network Vessel traffic flow prediction

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Forecasting stock volatility process using improved least square support vector machine approach

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SOFT COMPUTING 2019年第22期23卷 11867-11881页

作者： Gong, Xiao-Li Liu, Xi-Hua Xiong, Xiong Zhuang, Xin-Tian Qingdao Univ Sch Econ Qingdao 266061 Shandong Peoples R China Tianjin Univ Coll Management & Econ Tianjin 300072 Peoples R China China Ctr Social Comp & Analyt Tianjin 300072 Peoples R China Northeastern Univ Sch Business Adm Shenyang 110169 Liaoning Peoples R China

Considering that the stock returns distribution displays leptokurtosis as well as left-skewed properties, and the returns volatility process exhibits heteroscedasticity as well as clustering effects, the asymmetric GARCH-type models with non-Gaussian distributions (AGARCH-nG) are employed to describe the volatility process. In addition, the AGARCH-nG models are hybridized with artificial neural network (ANN) technique for forecasting stock returns volatility. Since the least square support vector machine (LS-SVM) technique displays strong forecast ability, we present an improved particle swarm optimization (IPSO) algorithm to optimize the parameters of LS-SVM technique in the process of stock returns volatility prediction. Then, we compare the forecasting performances of individual AGARCH-nG models, the hybrid AGARCH-nG-ANN methods and the data mining-based LS-SVM-IPSO method using stock markets data. The empirical results verify the effectiveness and superiority of the proposed method, which demonstrates that the LS-SVM-IPSO approach outperforms the AGARCH-type models with non-Gaussian distributions and those integrating with the artificial neural network methods.

关键词： Stock volatility forecasting Leptokurtosis distribution Artificial neural network Least square support vector machine particle swarm optimization algorithm

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A study of test and statistical damage constitutive model of multi-size polypropylene fiber concrete under impact load

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INTERNATIONAL JOURNAL OF DAMAGE MECHANICS 2019年第7期28卷 973-989页

作者： Yang, Xin Liang, Ninghui Liu, Xinrong Zhong, Zuliang Fujian Univ Technol Sch Civil Engn Fuzhou Fujian Peoples R China Fujian Prov Univ Key Lab Underground Engn Fuzhou Fujian Peoples R China Fujian Univ Technol Inst Geotech Engn Fuzhou Fujian Peoples R China Chongqing Univ Sch Civil Engn Chongqing 400045 Peoples R China Chongqing Univ Minist Educ Key Lab New Technol Construct Cities Mt Area Chongqing Peoples R China

To investigate the effects of mixing polypropylene fiber of different sizes and the effect of fiber size on the impact characteristics of concrete, two sizes of polypropylene fine fiber and one size of polypropylene coarse fiber were selected to design and fabricate nine groups of polypropylene fiber-reinforced concrete test specimens by controlling the fiber mixing ratio and conducting a split Hopkinson pressure bar test to obtain the stress-strain curves of the test specimens in various groups, and their parameters such as the elasticity modulus, peak strength, and peak strain. The incorporation of fiber improved the pre-peak impact properties of concrete to different extents. Strain hardening did not occur in the post-peak curves, and different types of fibers exhibited different characteristics. Thus, the fine fiber could significantly improve the peak strain, while the coarse fiber could more significantly improve the elasticity modulus and peak strength. The improving effects exerted by incorporating three types of fiber were better than those exerted by incorporating two types of fiber. Moreover, the statistical damage model was used to obtain the parameters by fitting and analyzing their variation rules based on the statistical damage constitutive model and the particle swarm optimization algorithm.

关键词： Different sizes polypropylene fiber-reinforced concrete split Hopkinson pressure bar test statistical damage constitutive model particle swarm optimization algorithm

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Optimal design and performance analysis of solar hybrid CCHP system considering influence of building type and climate condition

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ENERGY 2019年 174卷 647-663页

作者： Yang, G. Zhai, X. Q. Shanghai Jiao Tong Univ Inst Refrigerat & Cryogen Shanghai 200240 Peoples R China

Incorporating solar energy technologies to the conventional combined cooling, heating and power (CCHP) system has been considered as an effective solution to mitigate the looming energy and environmental challenges. The mathematical model of a conventional CCHP system hybridized with photovoltaic (PV) panels and solar thermal collectors is built in this study. The particle swarm optimization (PSO) algorithm is employed to find the optimal size of key components of the solar hybrid CCHP system. The simulation work of solar hybrid CCHP systems in three building prototypes across seven climate zones is carried out to find appropriate design schemes of these cases. Besides, some guiding principles of the design of the solar hybrid CCHP system in early stage are summarized. The results show that the system under the following thermal load (FTL) strategy is the first choice in most cases of hospitals and hotels, except for the systems of hotels in the cold and very cold zones. On the contrary, the systems in offices perform better in the following electric load (FEL) mode in the majority of climate zones except for the hot-humid zone. Generally, the average optimal integrated performance (S) values of hospitals, hotels and offices can reach 28.95%, 28.20% and 22.69%, respectively. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Combined cooling heating and power Solar energy Hybrid system particle swarm optimization algorithm Building energy consumption Climate condition

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Development of an expression for the volume of off-centered conical pile inside a cylindrical silo

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MEASUREMENT 2019年 146卷 903-911页

作者： Yigit, Enes Karamanoglu Mehmetbey Univ Fac Engn Dept Elect & Elect Engn TR-70100 Karaman Turkey

In this study, both analytical and empirical formula that expresses volume of off-centered conical pile inside a cylindrical structure are proposed. The total of 1050 off-centered cones including three parameters of dimension are acquired by computer aided design software. A volume expression enclosing conical pile parameters associated with variables of optimization is derived. Then the volume expression is obtained by substituting the variables determined by particle swarm optimization algorithm. The mean absolute percentage error between calculated and designed volumes of 1050 data are found as 0.0017% and 0.0299% for analytical and empirical expressions, respectively. Proposed calculations are also compared with another empirical formula existing in literature. In addition, two different small-scale case study are performed and the proposed formulations are verified experimentally. Results demonstrate that volume of the off-centered conical pile inside a cylindrical structure can be easily calculated with the help of empirically developed expression, without using complex mathematics. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Conical grain pile Grain level measurement particle swarm optimization algorithm

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Designing a New Artificial Lift Method Using Computational Simulation and Evolutionary optimization

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IEEE ACCESS 2019年 7卷 123846-123859页

作者： Moreira, Andre Pimentel Lepikson, Herman Augusto Schnitman, Leizer Bezerra Ramalho, Geraldo Luis Fed Inst Educ Sci & Technol Ceara Dept Ind BR-60040215 Fortaleza Ceara Brazil Mfg & Technol Integrated Campus SENAI Inst Innovat Automat & Logist BR-41650010 Salvador BA Brazil Univ Fed Bahia Ctr Technol Qualificat Ind Automat BR-40210630 Salvador BA Brazil

Oil exploitation has moved into deeper reservoirs with the advances in drilling techniques and thus the development of new pumping techniques has become a challenge to improve production. The positive displacement pumping system proposed in this paper is presented as an artificial lift technique. Here we present a new pumping device, its estimated operational curves through numerical simulations and a prototype of the device, denominated a Double Acting Submersible Linear Pump (SLP), which is a hydraulic system adapted for inside well operations. Design parameters used in the tests were optimized using the particle swarm optimization (PSO) algorithm to maximize oil production while optimizing the parameters (characterstics) of the pump, such as maximum pump diameters and submersible electric motor power, pressure and volumetric displacement of the hydraulic pump. Also presented are the operating curves estimated from the numerical simulation of the power and pump modules, as well as the SLP0 and SLP1 experimental test curves in a controlled environment using the optimized parameters obtained using PSO The data obtained were compared to the computational simulations performed with the Automation Studio (TM) software. The SLP0 and SLP1 optimal design tests showed the importance to delimit the range of operation within which the SLP must operate, since the pipes and hydraulic components must be suitable for the flow of hydraulic oil and the production oil. The findings presented here play an important role in the production process of a fully operational SLP prototype.

关键词： Artificial lift technique petroleum industry particle swarm optimization algorithm computational modeling and simulation

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An Improved particle Filter With a Novel Hybrid Proposal Distribution for Quantitative Analysis of Gold Immunochromatographic Strips

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IEEE TRANSACTIONS ON NANOTECHNOLOGY 2019年 18卷 819-829页

作者： Zeng, Nianyin Wang, Zidong Zhang, Hong Kim, Kee-Eung Li, Yurong Liu, Xiaohui Xiamen Univ Dept Instrumental & Elect Engn Xiamen 361005 Fujian Peoples R China Brunel Univ Dept Comp Sci Uxbridge UB8 3PH Middx England Korea Adv Inst Sci & Technol Dept Comp Sci Daejeon 305701 South Korea Fuzhou Univ Coll Elect Engn & Automat Fuzhou 350002 Fujian Peoples R China Fujian Key Lab Med Instrumentat & Pharmaceut Tech Fuzhou 350002 Fujian Peoples R China

In this paper, a novel statistical pattern recognition method is proposed for accurately segmenting test and control lines from the gold immunochromatographic strip (GICS) images for the benefits of quantitative analysis. A new dynamic state-space model is established, based on which the segmentation task of test and control lines is transformed into a state estimation problem. Especially, the transition equation is utilized to describe the relationship between contour points on the upper and the lower boundaries of test and control lines, and a new observation equation is developed by combining the contrast of between-class variance and the uniformity measure. Then, an innovative particle filter (PF) with a hybrid proposal distribution, namely, deep-belief-network-based particle filter (DBN-PF) is put forward, where the deep belief network (DBN) provides an initial recognition result in the hybrid proposal distribution, and the particle swarm optimization algorithm moves particles to regions of high likelihood. The performance of proposed DBN-PF method is comprehensively evaluated on not only an artificial dataset but also the GICS images in terms of several indices as compared to the PF and DBN methods. It is demonstrated via experiment results that the proposed approach is effective in quantitative analysis of GICS.

关键词： Gold immunochromatographic strip particle filter proposal distribution deep belief network dynamical model image segmentation particle swarm optimization algorithm Monte Carlo

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