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An Efficient Dynamic Priority-Queue algorithm Based on AHP and pso for Task Scheduling in Cloud Computing 1

16th International Conference on Hybrid Intelligent Systems (HIS) / 8th World Congress on Nature and Biologically Inspired Computing (NaBIC)

作者： Ben Alla, Hicham Ben Alla, Said Ezzati, Abdellah Touhafi, Abdellah Hassan 1 Univ LAVETE Lab Sci & Tech Fac Sci & Tech FacMath & Comp Sci Dept Settat 26000 Morocco Vrije Univ Brussel Dept Elect & Informat ETRO Pl Laan 2 B-1050 Brussels Belgium

ISBN: (数字)9783319529417

ISBN: (纸本)9783319529417;9783319529400

Nowadays Cloud Computing is an emerging technology in the area of parallel and distributed computing. Task scheduling is one of the major issues in cloud computing, which plays an important role to improve the overall performance and services of the cloud. Task scheduling in cloud computing means assign best suitable resources for the task to be executed with the consideration of different parameters like execution time, user priority, cost, scalability, throughput, makespan, resource utilization and so on. In this paper, we address the challenge of task scheduling, and we consider one of most critical issues in scheduling process such as the task priorities. The goal of this paper is to propose an efficient Dynamic Priority-Queue (DPQ) algorithm based on Analytic Hierarchy Process (AHP) with Particle Swarm Optimization (pso) algorithm. The proposed algorithm DPQ-pso gives full consideration to the dynamic characteristics of the cloud computing environment. Further, the proposed algorithm has been validated through the CloudSim simulator. The experimental results validate that the proposed approach can effectively achieve good performance, user priority, load balancing, and improve the resource utilization.

关键词： Cloud computing pso algorithm AHP DPQ-pso algorithm Task scheduling Dynamic queues

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A pso-RRBF neural network for BOD prediction in sewage treatment

A PSO-RRBF neural network for BOD prediction in sewage treat...

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第三十八届中国控制会议

作者： Minghui Chu Wenjing Li Junfei Qiao Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System

Aiming at the question that the effluent biochemical oxygen demand(BOD)in sewage treatment is difficult to measure accurately in real time,a soft-measurement model of recursive radial basis function(RRBF)neural network based on pso algorithm(pso-RRBF)is proposed to predict the effluent ***,the pso algorithm is put forward to determine not only the input variables but also the structure of the RRBF ***,the gradient descent method is used to adjust the weights,center and ***,the soft-measurement model is applied to the actual sewage treatment *** experimental results show that the soft-measurement model has a more compact structure and its accuracy is improved compared with other models.

关键词： pso algorithm recursive RBF gradient descent method effluent BOD soft measurement

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A Resource Optimization Allocation Strategy for China's Shipbuilding Industry Green Innovation System

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INTERNATIONAL JOURNAL OF INNOVATION AND TECHNOLOGY MANAGEMENT 2020年第4期17卷

作者： Bi, Kexin An, Kwangil Li, Xiang Harbin Engn Univ Sch Econ & Management Harbin 150001 Peoples R China Harbin Univ Sci & Technol Sch Econ & Management Harbin 150080 Peoples R China Kim Chaek Univ Technol Informat Ctr Pyongyang 950003 North Korea

In order to realize the resource optimization allocation in the green innovation system of China's shipbuilding industry under the internet environment, to improve the level of green innovation and to reduce the resource consumption, a resource optimization allocation model and the corresponding allocation strategy are proposed. The model integrates and shares the innovation resource data through Internet of Things (IOT) technology, and optimizes the allocation decision by using the cooperative differential game and Particle Swarm Optimization (pso) algorithm. At the same time, it ensures the robustness of green innovation system and realizes the optimization allocation of resources. A case study is given to illustrate the feasibility of the model. The results show that the green innovation subject can carry out strategic interaction by adjusting the allocation proportion of innovation resources through the proposed model, so as to optimize the overall green innovation benefits of the system.

关键词： Innovation resource allocation IOT technology cooperative differential game pso algorithm

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Power system stabilization using an optimal hybrid control strategy with a PID controller

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JOURNAL OF ELECTROMAGNETIC WAVES AND APPLICATIONS 2018年第17期32卷 2265-2280页

作者： Chaudhary, Rekha Singh, Arun Kumar Natl Inst Technol Elect Engn Dept Jamshedpur 831014 Jharkhand India NIT Kurukshetra Elect Engn Dept Kurukshetra Haryana India

As a power system turns out to be all the more heavily loaded and more unpredictable, maintaining stability becomes a difficult issue. In this paper, we propose an optimal control procedure for power system adjustment utilizing a proportional-integral-derivative (PID) controller in a coordinated power system. In modern applications, the most generally utilized controller is the PID controller, which necessitates productive techniques to control the distinctive parameters of the plant. The proposed strategy uses the particle swarm optimization algorithm-based way to deal with producing the optimal tuning parameters. This paper investigates the execution of the power system and dynamic execution of the controller is compared with the conventional controller with approval of the adequacy of the proposed mixture controller. Experimental results demonstrate that the proposed system has upgraded the system stability of the power system all the more adequately existing systems.

关键词： PID controllers pso algorithm stability optimal tuning power system

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A Novel Framework for FIR Digital Filter Design Based on P system with pso

A Novel Framework for FIR Digital Filter Design Based on P s...

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International Conference on Information Systems and Computer Aided Education (ICISCAE)

作者： Zhang, Chengfang Yue, Zhen Wang, Fengjuan Yan, Dan Sichuan Police Coll Luzhou Peoples R China Beijing Univ Posts & Telecommun Beijing Peoples R China Space Star Technol Co LTD Chengdu Co Chengdu Sichuan Peoples R China

ISBN: (纸本)9781538657386

Digital filter plays an important role in the digital signal processing, with a wide range of theoretical and practical application. The essential characteristic of the digital filter is that it can make the useful signal as much as possible all go through, while maximally suppress the useless signal. Due to the structure and function of digital filter, the classical filter design methods will suffer from some shortcomings: slow convergence and local minimums. To overcome the shortcomings in FIR filter design, a novel P systems-based method for optimal FIR filter design under the framework of P system is proposed by combining parallel property of P system, which is named as Tissue P Systems algorithm (TPS). In the proposed TPS model, instead of traditional design methods, a star tissue P system is designed as its computing framework, while the impulse response coefficients of the filter are regarded as the objects in the elementary membranes. Each object in cells expresses a group of filter coefficients to be optimized by using velocity-position model of pso, and inherent communication mechanism is used to share the objects among different elementary membranes, so as to determine the optimal filter parameters. Low-pass filter (LP), high-pass filter (HP), band-pass filter (BP) and band-stop filter (BS) are employed to verify the effectiveness of the proposed method. Five state-of-the-art design methods, which are Parks-McClellan (PM), real-coded GA (RGA), particle swarm optimization (pso), differential evolution (DE) and opposition-based harmony search (OHS) are used to exhibit the advantages of the proposed framework. The comparison results demonstrate the proposed TPS model that obtains better performance regarding magnitude response, with minimum stopband ripple, and maximum stopband attenuation. Moreover, the simulation results also show that the proposed FIR filter can close the requirements of ideal filter to the maximum extent.

关键词： FIR Filter P systems Membrane rules pso algorithm

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pso Optimized ANN Diagnosis of Early Gear Pitting

PSO Optimized ANN Diagnosis of Early Gear Pitting

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Prognostics and System Health Management Conference (PHM-Chongqing)

作者： Li, Jialin Qu, Yongzhi Hong, Liu He, David Northeastern Univ Coll Mech Engn & Automat Shenyang Liaoning Peoples R China Wuhan Univ Technol Sch Mech & Elect Engn Wuhan Hubei Peoples R China Univ Illinois Dept Mech & Ind Engn Chicago IL 60607 USA

ISBN: (纸本)9781538653807

Gear pitting diagnosis has always been an important research topic and different diagnostic methods are used. This paper uses artificial neural network (ANN) to diagnose early gear pitting. There are several issues that are inevitable with the application of ANN for diagnosis of early gear pitting: feature extraction, neural network structure optimization, and training stability. This paper proposes a new feature extraction method that uses fast Fourier transform (FFT) to select a number of frequencies in the frequency spectrum and use the frequency amplitudes as the inputs of ANN. The particle swarm optimization (pso) is used to optimize the initial value of the network to make the training more stable. Furthermore, the performance of the ANN diagnosis under different working conditions are also compared and analyzed in the paper. The proposed method has been validated through the data collected from the gear pitting test experiment. The validation results have shown that the faults diagnosis accuracy could reach 100%, which proves that the proposed method is reasonable.

关键词： Gear pitting pso algorithm FFT artificial neural network

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pso Based Traffic Optimization Approach for Railway Networks

PSO Based Traffic Optimization Approach for Railway Networks

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International Conference on Artificial Intelligence and Data Processing (IDAP)

作者： Yaman, Orhan Karakose, Ebru Karakose, Mehmet Firat Univ Dept Comp Engn Elazig Turkey Firat Univ Civil Aviat High Sch Elazig Turkey

ISBN: (纸本)9781538668788

In large cities, most people use more than one vehicle of transportation per day. With the increase of transportation vehicles, traffic problems also arise. Railway transportation is an indispensable type of transportation for crowded cities. In this study, a method based on Particle Swarm Optimization is proposed for railway traffic. On the one-way and two-way railway line, the trains are waiting for each other as they move. In this study, a method was developed to reduce the waiting time of trains. A simulation environment was established for the implementation of the developed method. A simulation with 12 stations on a one-way and two-way railway with a length of 340 km was developed and 11 trains were used. In the proposed method, the objective is to minimize the waiting times of the trains by creating the best voyage plan.

关键词： Railway timetable Railway scheduling Rail transportation Railway traffic optimization pso algorithm

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Optimization of PID using pso for Upper Limb Rehabilitation Robot 5

Optimization of PID using PSO for Upper Limb Rehabilitation ...

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5th IEEE International Conference on Engineering Technologies and Applied Sciences (IEEE ICETAS)

作者： Raza, Yarooq Ahmed, Syed Faiz Ali, Athar Joyo, M. Kamran Kadir, Kushsairy A. Univ Kuala Lumpur British Malaysian Inst Kuala Lumpur Kuala Lumpur Malaysia Univ Trento Trento Italy

ISBN: (纸本)9781538679661

Proportional Integral Derivative (PID) controller is most widely used feedback controller used in various applications such as control systems, instrumentation and motor drives. Tuning of PM parameters is challenging to researchers, despite of its popularity. This paper presents the optimization of PM controller using particle swarm optimization for controlling upper limb rehabilitation robot. The proposed algorithm offers easy implementation, less computation burden and fast convergence of the system response. pso is an evolutionary algorithm which is based on the social behavior of fish and flocking of birds. This study will demonstrate in brief how to employ Particle swarm optimization on PID. MATLAB simulations have been carried out and graphs have been plotted which shows less percent overshoot and a significant rise time of the system having no steady state error. The results proved that pso-PID is stable and efficient control algorithm for upper limb rehabilitation robot.

关键词： PID controller pso algorithm Upper limb rehabilitation robot

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Surface roughness modeling and milling parameter optimization of 15-5PH stainless steel in milling arc surface

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JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2018年第4期40卷 1-9页

作者： Zheng, Kan Yao, Jingdong Dong, Song Nanjing Univ Sci & Technol Sch Mech Engn Nanjing 210094 Jiangsu Peoples R China Cent South Univ Changsha 410006 Hunan Peoples R China

In this study, a comprehensive prediction model of surface roughness on arc surface of 15-5PH stainless steel is constructed, which is considered to consist of the certain part and uncertain part. The certain part considered to be dependent on path interval and scallop height is formulated in theory using kinematics analysis and geometric modeling. The uncertain part related to the machining system stability, cooling condition and milling modes is empirically predicted by partial least squares based on experimental data. Then, the prediction model is integrated into particle swarm optimization algorithm to find the optimal parameters for the maximum material removal rate (MRR). Finally, the verification experiments considering validation of prediction model and optimal parameters are conducted. The experimental results show that the prediction accuracy presents well with the mean prediction error 10.49%. The optimization also satisfies the constraint of surface roughness with a 262% increase in MRR.

关键词： Milling Surface roughness Partial least squares pso algorithm Parameters optimization

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Investigation on Optimization Design of Offshore Wind Turbine Blades based on Particle Swarm Optimization

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ENERGIES 2019年第10期12卷 1972页

作者： Ma, Yong Zhang, Aiming Yang, Lele Hu, Chao Bai, Yue Sun Yat Sen Univ Sch Marine Engn & Technol Guangzhou 518000 Guangdong Peoples R China Harbin Engn Univ Coll Shipbldg Engn Harbin 150001 Heilongjiang Peoples R China Southern Marine Sci & Engn Guangdong Lab Zhuhai Zhuhai 519000 Peoples R China

Offshore wind power has become an important trend in global renewable energy development. Based on a particle swarm optimization (pso) algorithm and FAST program, a time-domain coupled calculation model for a floating wind turbine is established, and a combined optimization design method for the wind turbine's blade is developed in this paper. The influence of waves on the power of the floating wind turbine is studied in this paper. The results show that, with the increase of wave height, the power fluctuation of the wind turbine increases and the average power of the wind turbine decreases. With the increase of wave period, the power oscillation amplitude of the wind turbine increases, and the power of the wind turbine at equilibrium position decreases. The optimal design of the offshore floating wind turbine blade under different wind speeds is carried out. The results show that the optimum effect of the blades is more obvious at low and mid-low wind speeds than at rated wind speeds. Considering the actual wind direction distribution in the sea area, the maximum power of the wind turbine can be increased by 3.8% after weighted optimization, and the chord length and the twist angle of the blade are reduced.

关键词： wind turbine pso algorithm FAST time-domain coupled model blade optimization

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