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VIRTUAL MACHINE DEPLOYMENT STRATEGY BASED ON IMPROVED PSO IN CLOUD COMPUTING

COMPUTING AND INFORMATICS 2020年第1-2期39卷 83-104页

作者： Pang, Shanchen Dong, Dekun Wang, Shuyu China Univ Petr Coll Comp Sci & Technol Qingdao 266580 Peoples R China

Energy consumption is an important cost driven by growth of computing power, thereby energy conservation has become one of the major problems faced by cloud system. How to maximize the utilization of physical machines, reduce the number of virtual machine migrations, and maintain load balance under the constraints of physical machine resource thresholds that is the effective way to implement energy saving in data center. In the paper, we propose a multi-objective physical model for virtual machine deployment. Then the improved multi-objective particle swarm optimization (TPSO) is applied to virtual machine deployment. Compared to other algorithms, the algorithm has better ergodicity into the initial stage, improves the optimization precision and optimization efficiency of the particle swarm. The experimental results based on CloudSim simulation platform show that the algorithm is effective at improving physical machine resource utilization, reducing resource waste, and improving system load balance.

关键词： Cloud computing Pareto optimal solution particle swarm optimization algorithm resource reservation virtual machine deployment

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Model of Improved a Kernel Fast Learning Network Based on Intrusion Detection System

Model of Improved a Kernel Fast Learning Network Based on In...

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1st International Conference on Intelligent Computing and optimization (ICO)

作者： Ali, Mohammed Hasan Zolkipli, Mohamed Fadli Univ Malaysia Pahang Fac Comp Syst & Software Engn Gambang 26300 Malaysia

ISBN: (纸本)9783030009793;9783030009786

The detection of network attacks on computer systems remains an attractive but challenging research scope. As network attackers keep changing their methods of attack execution to evade the deployed intrusion-detection systems (IDS), machine learning (ML) algorithms have been introduced to boost the performance of the IDS. The incorporation of a single parallel hidden layer feed-forward neural network to the Fast Learning Network (FLN) architecture gave rise to the improved Extreme Learning Machine (ELM). The input weights and hidden layer biases are randomly generated. In this paper, the particle swan optimization algorithm (PSO) was used to obtain an optimal set of initial parameters for Reduce Kernel FLN (RK-FLN), thus, creating an optimal RKFLN classifier named PSO-RKELM. The derived model was rigorously compared to four models, including basic ELM, basic FLN, Reduce Kernel ELM (RK-ELM), and RK-FLN. The approach was tested on the KDD Cup99 intrusion detection dataset and the results proved the proposed PSO-RKFLN as an accurate, reliable, and effective classification algorithm.

关键词： Fast learning network Kernel extreme learning machine KDD Cup99 particle swarm optimization algorithm Intrusion detection system

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A direct numerical method for solving the Foppl-Hencky equation 12

A direct numerical method for solving the Foppl-Hencky equat...

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12th International Symposium on Computational Intelligence and Design (ISCID)

作者： Zhao, Min Zhang, Xiaoping Sun, Yongping Commun Univ Zhejiang Coll Media Engn Hangzhou Peoples R China

ISBN: (纸本)9781728146522

We rewrite the Foppl-Hencky equation as an unconstrained optimization, and then solve the equation by an optimization algorithm. The optimization algorithm is constructed by combining the intelligent algorithms with an numerical algorithm for different equation. We give three numerical experiments, and the simulation results show that the Rounge-Kutta method based particle swarm optimization algorithm solves the equation very well.

关键词： Foppl-Hencky equation particle swarm optimization algorithm Runge-Kutta method

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Joint Power and Channel Allocation Based on Caching with User Preferences in D2D HetNets

Joint Power and Channel Allocation Based on Caching with Use...

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IEEE/CIC International Conference on Communications in China (ICCC)

作者： Lyu, Xinpeng Wang, Ying Liu, Man Li, Zhendong Fei, Zixuan Ning, Weichen Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

ISBN: (纸本)9781728107325

Device-to-device (D2D) and caching can effectively solve the problem of repeated transmission of plentiful short videos in wireless networks. This paper considers a downlink video transmission process among cellular users and D2D users with interference between co-channel users. Caching with user preferences in D2D users is also taken into consideration to improve the system capacity. Joint channel and power allocation is optimized to maximize the sum rate of all users. The problem is a mixed integer nonlinear programming (MINLP) and is hard to solve. Firstly, the concept of cache similarity distance based on user preferences and video popularity is introduced to divide users into different groups. Then, the optimization problem is decoupled into two sub-problems. And a joint channel and power allocation algorithm based on caching with user preferences is proposed. Besides, a penalty function is introduced to handle constraints and a speed update function is modified to optimize binary variables and continuous variables simultaneously. Finally, an improved particle swarm optimization algorithm (PSO) is proposed to solve the problem. Simulation results show that the proposed algorithm can effectively improve the system capacity of the network.

关键词： Device-to-device user preferences channel power particle swarm optimization algorithm

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Logistics Distribution Location Based on particle swarm optimization algorithm

Logistics Distribution Location Based on Particle Swarm Opti...

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World Automation Congress

作者： Xichun CHEN Junli WANG School of Traffic and Transportation Engineering Lanzhou Jiaotong University Lanzhou 730070 China Gansu Province Supervision and Inspection Center of Product Quality Lanzhou 730030 China

ISBN: (纸本)9781467344975

The aim of logistics distribution center location is to improve the logistics service quality and reduce the transportation cost of goods. The problems are related to both the classical location problem and the vehicle routing problem. This paper proposes logistics distribution center location based on particle swarm optimization algorithm. It models the logistics distribution center location problem considering the user demand and operating cost, then it uses modified particle swarm optimization algorithm to give solution. Experimental result shows that the proposed model and algorithm is effective.

关键词： distribution center location particle swarm optimization algorithm optional point

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Bidding Strategy of Thermal Units Participating in Real-Time Depth Peak Load Regulation Ancillary Service Market based on Stackelberg Game

Bidding Strategy of Thermal Units Participating in Real-Time...

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Asia Power and Energy Engineering Conference (APEEC)

作者： Yang, Jianlin Guo, Mingxing Fei, Fei Gong, Kai Wang, Xu Jiang, Chuanwen State Grid Corp China Shanghai Municipal Elect Power Co Shanghai 200122 Peoples R China Shanghai Jiao Tong Univ Elect Informat & Elect Engn Shanghai 200240 Peoples R China

ISBN: (纸本)9781728115900

With the development of the peak load regulation ancillary service market and the improvement of the rules, the role of thermal power unit changed from the passive participant in peak load regulation for duty to the active bidder. The power grid dispatching agency called the depth peak load regulation (DPLR) resources on demand according to the price. In order to research the optimal bidding strategy of thermal power units in the depth peak load regulation ancillary service market, a bi-level model of multiple thermal power manufacturers and single market dispatching institution is established based on stackelberg game. The upper level is the bidding strategy of thermal power manufacturers, including the income and cost of thermal power manufacturers. The lower level is the optimal scheduling scheme of the market dispatching institution. The nonlinear bi-level model is solved by the particle swarm optimization algorithm. The cases show the importance and validity of the proposed model

关键词： bi-level optimal model stackelberg game particle swarm optimization algorithm depth peak load regulation

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Design of PI^λD^δ-Heart Rate Controllers for Cardiac Pacemaker 19

Design of PI<SUP>λ</SUP>D<SUP>δ</SUP>-Heart Rate Controlle...

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19th IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)

作者： Momani, Shaker Batiha, Iqbal M. El-Khazali, Reyad Ajman Univ Coll Humanities & Sci Dept Math & Sci Ajman U Arab Emirates Univ Jordan Fac Sci Dept Math Amman 11942 Jordan Khalifa Univ ECCE Dept Abu Dhabi 127788 U Arab Emirates

ISBN: (纸本)9781728153414

An arrhythmia is any disorder of your heart rate or rhythm. It means that the heartbeats too quickly, too slowly, or with an irregular pattern. Most arrhythmias result from problems in the electrical system of the heart. A cardiac pacemaker is a medical device that invented for those reasons. It helps the suffering patient to control abnormal heart rhythms. Several proportional-integral-derivative (PID) controllers have been proposed for improving the performance of such device, but it was shown that each of them has some limitations in facing the dynamic challenge of regulating heart rate. In this paper, we propose a robust fractional-order PID ((PID delta)-D-lambda) controller to improve that performance. This controller has been designed based on particle swarm optimization (PSO) algorithm via El-Khazali's approach. To illustrate the proposed design method, the objective function of the model under consideration is presented as well as the results of using the (PID delta)-D-lambda-controller are compared with the results of using the conventional PID-controller to minimize several error functions;ITAE, IAE, ISE and ITSE. These results of such comparisons that are related to step response specifications, allow us to see the effectiveness of the best controller. Among of all these performance indices, the ITAE in case of the (PID delta)-D-lambda-controller, shows more capability than others in providing closed-loop response with minimal overshoot, fast rise time and fast settling time. Further, to illustrate the influence and the efficiency of the proposed design method, the PSO-(PID delta)-D-lambda-controller via El-Khazali's approach is compared with that of the same using two well-known approximations for s(+/-alpha);i.e., the Continued Fractional Expansion (CFE) and Oustaloup's approximations. The main results of this work are verified via numerical simulations.

关键词： cardiac pacemaker (PID delta)-D-lambda-controller particle swarm optimization algorithm Laplacian operator El-Khazali's approach

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optimization of Vehicle Management for One-Way Carsharing Systems Based on Time Windows of Users 8

Optimization of Vehicle Management for One-Way Carsharing Sy...

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8th International Conference on Industrial Technology and Management (ICITM)

作者： Shen, Ling Tao, Fengming Chongqing Univ Coll Mech Engn Chongqing Peoples R China Chongqing Univ Sch Econ & Business Adm Chongqing Peoples R China

ISBN: (纸本)9781728132686

In order to reduce the operating costs of car-sharing companies and improve the satisfaction of users, a one-way carsharing systems optimization model which considers the time windows requirements of users is constructed with minimum total costs including scheduling costs and penalty costs. Then, an improved particle swarm optimization algorithm with tabu search is proposed to solve the model. Numerous experiments are performed to obtain the optimal solution. Finally, based on experimental results, some practical proposals for one-way carsharing systems are provided for the carsharing companies.

关键词： vehicle scheduling carsharing time windows particle swarm optimization algorithm tabu search

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Soft Sensor Measurement Research on Resolution Ratio of Cement Kiln Tail Based on IPSO-SVR

Soft Sensor Measurement Research on Resolution Ratio of Ceme...

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Chinese Automation Congress (CAC)

作者： Xu, Ting Leng, Miao Guo, Pan Gong, Xin Li, Xuejun Zhang, Liying Northeast Normal Univ Sch Informat Technol & Engn Coll Humanities & Sci Changchun Peoples R China State Grid Jilin Elect Power Co Ltd Liaoyuan Power Supply Co Power Dispatch Control Ctr Liaoyuan Peoples R China

ISBN: (纸本)9781728140940

In order to realize the real-time on-line detection on resolution ratio of cement kiln tail, by means of the advantages from soft sensor measurement technology in which process parameters cannot be directly measured at present, a support vector regression (SVR) optimized by the improved particle swarm optimization (IPSO) algorithm is proposed. Based on the idea of adaptive weight, this algorithm overcomes the shortcomings of PSO which is prone to show premature convergence and poor local search ability, and improves its global search ability and local improvement ability. The parameters of SVR machine are optimized, and then soft sensor measurement model of resolution ratio of cement kiln tail is established. The simulation is compared with those based on the cross validation method and PSO, the results show that the IPSO-SVR algorithm has better ability of modeling, prediction and generalization, and the average relative error of prediction is 0.75%, so it can be further applied to the product real-time on-line detection on large-scale industries such as cement production.

关键词： soft sensor measurement particle swarm optimization algorithm support vector regression machine

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Virtual Force Senor Based on PSO-BP Neural Network for Legged Robots in Planetary Exploration 12th

Virtual Force Senor Based on PSO-BP Neural Network for Legge...

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12th International Conference on Intelligent Robotics and Applications (ICIRA)

作者： Wang, Chu Wu, Shuang Chen, Lei Liu, Bin Wei, Qingqing Wang, Yaobing Beijing Inst Spacecraft Syst Engn Beijing Key Lab Intelligent Space Robot Syst Tech Beijing 100094 Peoples R China

ISBN: (纸本)9783030275389;9783030275372

The foot force of the legged robot plays a decisive role in the balance of the fuselage. Especially when walking on the irregular road surface and the less rigid road surface, the change of the foot end support force will change the attitude of the robot body, which will affect the stability. In addition, the change of foot force is also closely related to the flexibility of the movement of the leg of the robot. When the movement of the foot end has a transition from free space to constrained space, only the position control will not meet the requirements of the leg for the flexibility of the movement. This paper addresses the design of virtual sensors for terrain adaptation developed with the aims of simplifying the hardware of the legged robot or increasing the reliability of the sensorial information available. The virtual force sensor (VFS) is developed based on particle swarm optimization (PSO) BP neural network and can estimate the forces exerted by the feet from data extracted from joint-position, joint-velocity, and joint-torque, which are mandatory in all robotic systems. The force estimates are used to detect foot/ground contact. Several simulations carried out with the hexapod robot are reported to prove the efficacy of this method. This method simplifies the hardware of the robot, reduces design, construction and maintenance costs while enhancing the robustness of the robot and the reliability of its behavior.

关键词： Virtual force sensor particle swarm optimization algorithm BP neural network Legged robot

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