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Fairness evaluation method of resource allocation based on BPSO multidimensional perspective

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第2期22卷 S4283-S4290页

作者： Lei, Jiao de Menezes, Rui Manuel Campilho Pereira ISCTE Univ Inst Lisbon Dept Quantitat Methods Management & Econ Lisbon Portugal Southern Med Univ Sch Hlth Serv Management Guangzhou Guangdong Peoples R China

In order to improve the effectiveness of fairness evaluation algorithm for public health resources, a kind of fairness evaluation method for public health resources based on BPSO dimension reduction under multi-perspective was proposed in the thesis. Firstly, data sources and research methods for fairness evaluation of public health resources was introduced;computational steps of Lorenz index were given;then, in order to reduce the complexity of data processing, particle swarm optimization was used to implement dimension reduction processing for software failure data;character string (0 or 1) of binary system was used to show particle position so as to realize simplification of data processing. Finally, effectiveness of methods mentioned in the thesis is verified in the positive analysis of fairness evaluation for regional distribution of health resources in Shaoguan city, Guangdong province.

关键词： particle swarm optimization algorithm Dimension reduction Multi-perspective Public health resources Fairness

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MULTI-WAYPOINT-BASED PATH PLANNING FOR FREE-FLOATING SPACE ROBOTS

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INTERNATIONAL JOURNAL OF ROBOTICS & AUTOMATION 2019年第5期34卷 461-467页

作者： Zhao, Suping Siciliano, Bruno Zhu, Zhanxia Gutierrez-Giles, Alejandro Luo, Jianjun Northwestern Polytech Univ Natl Key Lab Aerosp Flight Dynam 127 Youyi West Rd Xian 710072 Shaanxi Peoples R China Univ Naples Federico II Dept Elect Engn & Informat Technol Via Claudio 21 I-80125 Naples Italy

This paper studies the multi-waypoint-based path planning problem (MWPP) for redundant space robots. The end-effector of a space robot should visit a set of predefined waypoints with optimal distance, and the free-floating base should suffer minimum attitude disturbances from the manipulator during manoeuver. The MWPP is decomposed into two sub-problems: the problem of optimal waypoint-sequence and the problem of optimal joint-movements. First, the Hybrid Self-adaptive particle swarm optimization algorithm is proposed for optimal waypoint-sequence. Second, an Improved particle swarm optimization algorithm, combined with direct kinematics of the space robot, is proposed for optimal jointmovements. Finally, simulations are presented to validate the approach, including comparisons with other approaches.

关键词： Multi-waypoint-based path planning problem free-floating base redundant space robots waypoint-sequence particle swarm optimization algorithm

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Task scheduling in a cloud computing environment using HGPSO algorithm

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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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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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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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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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