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Strategic behavior and social optimization in a constant retrial queue with the N-policy

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2017年第3期256卷 841-849页

作者： Wang, Jinting Zhang, Xuelu Huang, Ping Beijing Jiaotong Univ Dept Math Beijing 100044 Peoples R China

We consider customers' strategic behavior and the corresponding social maximization problem in an M/M/1 constant retrial queue with the N-policy. There is no waiting space in front of the server and customers who find the server busy have to abandon the system, but they can leave their contact details to stay on a waiting list. Consequently, after a service completion, the server will seek a customer from the waiting list at a constant retrial rate on an FCFS basis. The server is switched off whenever the system becomes empty, and is resumed only when the number of waitlisted customers reaches a given threshold. We assume that the arriving customers who find the server busy or down decide whether to leave their contact details or to balk based on a linear reward-cost structure. We examine customers' strategic response to this mechanism and compare it to the social optimal behavior with delay information. It is shown that both Follow-the-Crowd (FTC) and Avoid-the-Crowd (ATC) behaviors exist in our system, and therefore, both multiple and unique equilibrium arrival rates could exist. Through the particle swarm optimization (PSO) algorithm, we numerically obtain the optimal solution of the social welfare maximization problem. The individual equilibrium strategies are compared with the social optimum and the Price of Anarchy (PoA) is studied as a measure to quantify the inefficiency of the equilibrium strategies. Finally, numerical examples are presented to illustrate the sensitivity of some key system performance measures. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Queueing Equilibrium strategies N-policy particle swarm optimization algorithm Social optimization

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Low-speed rolling bearing fault diagnosis based on EMD denoising and parameter estimate with alpha stable distribution

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2017年第4期31卷 1587-1601页

作者： Xiong, Qing Xu, Yanhai Peng, Yiqiang Zhang, Weihua Li, Yongjian Tang, Lan Xihua Univ Key Lab Fluid & Power Machinery Minist Educ Chengdu 610039 Sichuan Peoples R China Xihua Univ Sch Automobile & Transportat Chengdu 610039 Sichuan Peoples R China Southwest Jiaotong Univ State Key Lab Tract Power Chengdu 610031 Sichuan Peoples R China

When low-speed rolling bearings fail, it is hard to diagnose the extent of their damage. We developed a test rig to simulate the lowspeed rolling bearing operating condition, where bearings with various fault states are installed on the test wheelset and subjected to the same external loading condition. The collected bearing box acceleration time histories are processed with the Empirical mode decomposition (EMD) method combined with kurtosis criterion to filter the trend and noise components. Five characteristic parameters of Alpha stable distribution (ASD) are identified by fitting the ASD distribution to the vibration acceleration signals and computing the Probability density function (PDF). To highlight the advantage of ASD method in feature extraction, kurtosis also has be calculated. Through sensitivity and stability analysis of the six parameters and utilization of Least squares support vectors machine (LSSVM) with particle swarm optimization (PSO), three most sensitive and stable feature parameters including the characteristic exponent alpha, the scale factor gamma and the peak value of the PDF h are located and applied to evaluate the low-speed rolling bearings' damage position and damage extent. The proposed method was validated by test data, and the results demonstrated that the ASD characteristics combined with PSO-LSSVM can not only achieve fault diagnosis of low-speed rolling bearings' damage position and damage extent, but also have better diagnosis accuracy and operational efficiency than other methods.

关键词： Low-speed rolling bearing Fault diagnosis Empirical mode decomposition Alpha stable distribution particle swarm optimization algorithm Least squares support vector machine

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A quick control strategy based on hybrid intelligent optimization algorithm for planar n-link underactuated manipulators

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INFORMATION SCIENCES 2017年 420卷 148-158页

作者： Wang, Yawu Lai, Xuzhi Chen, Luefeng Ding, Huafeng Wu, Min China Univ Geosci Sch Automat Wuhan 430074 Hubei Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan 430074 Hubei Peoples R China China Univ Geosci Sch Mech Engn & Elect Informat Wuhan 430074 Hubei Peoples R China

This paper presents a quick two-stage position control strategy based on a hybrid intelligent optimization algorithm for a planar n-link underactuated manipulator with a passive first joint. In stage 1, the system is directly reduced to a planar virtual Acrobot by controlling n-2 active links to their target angles. A hybrid intelligent optimization algorithm, which includes genetic algorithm (GA) and particle swarm optimization algorithm (PSO), is used to solve all link target angles according to the target position of the system. By coordinating GA and PSO, the hybrid intelligent optimization algorithm ensures that all link target angles, the angle of the passive link at the end of stage 1, and the initial angle of the active link of the planar virtual Acrobot meet the angle constraint of the planar virtual Acrobot. So, the position control objective of the planar n-link underactuated manipulator is realized by controlling the active link of the planar virtual Acrobot to its target angle in stage 2. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Planar underactuated manipulator Genetic algorithm particle swarm optimization algorithm Angle constraint

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Super-Resolution Reconstruction From Multiple Defocused Infrared Images of Stationary Scene

IEEE PHOTONICS JOURNAL

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IEEE PHOTONICS JOURNAL 2017年第5期9卷

作者： Mao, Yuxing Zhao, Benjiang Yan, Dongmei Jia, Haiwei Chongqing Univ State Key Lab Power Transmiss Equipment & Syst Se Chongqing 400044 Peoples R China Hangzhou Yuanzhou Med Technol Co Ltd Hangzhou 310052 Zhejiang Peoples R China

Infrared image has poor visual effect for its low resolution. Super-resolution reconstruction (SRR) is an effective means to address this problem. Existing SRR algorithms use well-focused images and ignore the value of defocused images generated by the infrared imaging system during focusing. The basic idea of the present study is to treat a defocused infrared image as distribution and accumulation of scene information among different pixels of the infrared detector, as well as a valid observation of the imaged subject;defocused images are the result of blurring a corresponding high resolution (HR) image using a point spread function (PSF) followed by downsampling. From this idea, we used multiple defocused images to build an observation model for HR images and propose an SRR algorithm to approach the HR images. We have developed an image degradation model by analyzing optical lens imaging, using the particle swarm optimization algorithm to estimate the PSF of the HR image, and using compressed sensing theory to implement SRR based on the noncoherent characteristics of the defocused infrared images. Experiments demonstrate that our method can be used to obtain more information about details of a scene and improve the visual effect without adding any hardware facilities, improving the recognition and interpretation of the image subject.

关键词： Defocused infrared image super-resolution reconstruction point spread function compressed sensing particle swarm optimization algorithm

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A comparative study of multiple linear regression, artificial neural network and support vector machine for the prediction of dissolved oxygen

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HYDROLOGY RESEARCH 2017年第5期48卷 1214-1225页

作者： Li, Xue Sha, Jian Wang, Zhong-liang Tianjin Normal Univ Tianjin Key Lab Water Resources & Environm Tianjin 300387 Peoples R China

Dissolved oxygen (DO) is an important indicator reflecting the healthy state of aquatic ecosystems. The balance between oxygen supply and consuming in the water body is significantly influenced by physical and chemical parameters. This study aimed to evaluate and compare the performance of multiple linear regression (MLR), back propagation neural network (BPNN), and support vector machine (SVM) for the prediction of DO concentration based on multiple water quality parameters. The data set included 969 samples collected from rivers in China and the 16 predicted variables involved physical factors, nutrients, organic substances, and metal ions, which would affect the DO concentrations directly or indirectly by influencing the water-air exchange, the growth of water plants, and the lives of aquatic animals. The models optimized by particle swarm optimization (PSO) algorithm were calibrated and tested, with nearly 80% and 20% data, respectively. The results showed that the PSO-BPNN and PSO-SVM had better predicted performances than linear regression methods. All of the evaluated criteria, including coefficient of determination, mean squared error, and absolute relative errors suggested that the PSO-SVM model was superior to the MLR and PSO-BPNN for DO prediction in the rivers of China with limited knowledge of other information.

关键词： back propagation neural network multiple linear regression particle swarm optimization algorithm support vector machine water quality parameters

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Evaluating the performance of genetic and particle swarm optimization algorithms to select an appropriate scenario for forecasting energy demand using economic indicators: residential and commercial sectors of Iran

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INTERNATIONAL JOURNAL OF ENERGY AND ENVIRONMENTAL ENGINEERING 2015年第4期6卷 345-355页

作者： Nazari, Hesam Kazemi, Aliyeh Hashemi, Mohammad-Hosein Sadat, Mir Mahmoud Nazari, Mahboobeh Univ Tehran Fac Management Chamran HighwayPOB 14155-6311 Tehran Iran Shahid Beheshti Univ Fac Power & Water Shahid Abbaspour Tehran Iran Avicenna Res Inst ACECR Nanobiotechnol Res Ctr Tehran Iran

Energy supply security is one of the strategic issues of all states. In Iran, about 35 % of the total energy is consumed by the residential and commercial sectors. According to the importance of residential and commercial sectors in energy consumption, this paper develops different models to analyze energy demand of residential and commercial sectors. The GA and PSO energy demand estimation models (GA-DEM, PSO-GEM), a suitable model for this study, is used to estimate future energy demand of the sectors. Energy demand of these sectors has been estimated in two various forms, exponential and linear models. These sectors consumption in Iran from 1967 to 2010 is considered as the case of this study. The available data are partly used for finding the optimal, or near-optimal values of the coefficient parameters (1967-2006) and partly for testing the models (2007-2010). Our results show that PSO-DEM exponential model with inputs including, value added of all economic sectors, value of made buildings, the population and the electrical and fuel appliance price index using the mean absolute percentage error on test data is the most suitable model. Finally, based on the best scenario, the energy demand of residential and commercial sectors is estimated 1718 mega barrel of crude oil equivalent (MBOE) (1 barrel = 0.159 m(3)) up to the year 2032.

关键词： Forecasting Residential and commercial sectors Energy demand Genetic algorithm particle swarm optimization algorithm

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A social spider optimized image fusion approach for contrast enhancement and brightness preservation

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APPLIED SOFT COMPUTING 2017年 52卷 575-592页

作者： Maurya, Lalit Mahapatra, Prasant Kumar Kumar, Amod CSIR Cent Sci Instruments Org Biomed Instrumentat Div V2 Sect 30C Chandigarh 160030 India Natl Inst Tech Teachers Training & Res Dept Elect & Commun Engn Sect 26 Chandigarh 160019 India

Image enhancement means to process the image in order to obtain a visually pleasing effect on image with more perception details and less noise output. In this work, a social spider optimization based algorithm is used to produce two enhanced images, one having high sharpness or contrast with maximum entropy and the other having high peak signal-to-noise ratio (PSNR) sharp image. The two enhanced images are fused to obtain an output image which has an optimal trade-off between sharpness and noise. The proposed algorithms are applied to lathe tool images as well as to some other standard images to verify their effectiveness. The results are compared with conventional image enhancement techniques such as Histogram Equalization (HE), Linear Contrast Stretching (LCS), and Standard particle swarm optimization (PSO) algorithm. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Social spider optimization particle swarm optimization algorithm Image enhancement Image fusion Histogram equalization Linear contrast stretching

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Clustering performance comparison of new generation meta-heuristic algorithms

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KNOWLEDGE-BASED SYSTEMS 2017年 130卷 1-16页

作者： Ozbakir, Lale Turna, Fatma Erciyes Univ Ind Engn Dept TR-38039 Kayseri Turkey

This article addressed two new generation meta-heuristic algorithms that are introduced to the literature recently. These algorithms, proved their performance by benchmark standard test functions, are implemented to solve clustering problems. One of these algorithms called Ions Motion optimization and it is established from the motions of ions in nature. The other algorithm is Weighted Superposition Attraction and it is predicated on two fundamental principles, which are "attracted movements of agents" and "superposition". Both of the algorithms are applied to different benchmark data sets consisted of continuous, categorical and mixed variables, and their performances are compared to particle swarm optimization and Artificial Bee Colony algorithms. To eliminate the infeasible solutions, Deb's rule is integrated into the algorithms. The comparison results indicated that both of the algorithms, Ions Motion optimization and Weighted Superposition Attraction, are competitive solution approaches for clustering problems. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Cluster analysis Ions motion algorithm Weighted superposition attraction algorithm particle swarm optimization algorithm Artificial bee colony algorithm

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An optimal design to provide combined cooling, heating, and power of residential buildings

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INTERNATIONAL JOURNAL OF MODELLING AND SIMULATION 2018年第4期38卷 216-231页

作者： Keynia, Farshid Grad Univ Adv Technol Inst Sci & High Technol & Environm Sci Dept Energy Management & Optimizat Kerman Iran

Providing electricity for the residential buildings which devote a high portion of energy consumption is very crucial all over the world. Not only electrical loads but also heating and cooling loads are required to be supplied in a building. The aim of this paper is to supply cooling, heating, and electrical loads in a building by separating generation system and cogeneration system. In this regard, the optimal problem was developed to minimize the total costs using particle swarm optimization algorithm. As a numerical study, a high-rise building with 72 units located in Kerman is analyzed for eight different *** results show that in order to supply cooling, heating, and power, a cogeneration system consisting of a 195 kW micro gas turbine as prime mover, a 281 kW single-effect absorption chiller, a 439 kW air cooling compaction chiller, an 187 kW auxiliary boiler to compensate the heat load, and a 52.8 kW photovoltaic to generate employed electrical loads, is the best optimal system to supply the base building loads. In this optimized system, the annual electricity sales revenue is 93,251$, the annual cost of buying power is 7001$, the cost of buying fuel as annual consumption is 15,852.4$, and the annual production of carbon dioxide emissions in the building is 229.78 tons. The return on investment period for the aforementioned project is also estimated 4.98 years.

关键词： Building electrical heating and cooling loads cogeneration and separate generation systems particle swarm optimization algorithm

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Efficient detection algorithm of high frequency clutter data in geological exploration

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JOURNAL OF DISCRETE MATHEMATICAL SCIENCES & CRYPTOGRAPHY 2018年第2期21卷 339-344页

作者： Huang, Yanran Qin, Mingyang Yu, Ye Hunan Univ Sci & Technol Hunan Prov Key Lab Shale Gas Resource Utilizat Xiangtan 411201 Peoples R China Cent S Univ Sch Geosci & Infophys Engn Changsha 410083 Hunan Peoples R China

During the detecting process of geological exploration data, there is a variety of noise interferences, resulting in inaccurate data detection. When the current detection algorithm is used to detect high frequency Butter data, detection efficiency is low. 'lb this end, a detection algorithm of high frequency clutter data based on optimized BP neural network particle swarm algorithm is proposed. First, improved wavelet threshold denoising algorithm is utilized for clutter data denoising. Then, an optimized BP neural network particle swarm algorithm is employed to detect the clutter data. Experimental results show that the proposed algorithm improves the accuracy of data detection.

关键词： Geological exploration High frequency clutter data Wavelet threshold particle swarm optimization algorithm

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