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检索条件"主题词=multi-objective particle swarm optimization"

共 366 条记录，以下是71-80 订阅

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Task Scheduling optimization in Cloud Computing Applying multi-objective particle swarm optimization

Task Scheduling Optimization in Cloud Computing Applying Mul...

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11th International Conference on Service Oriented Computing (ICSOC)

作者： Ramezani, Fahimeh Lu, Jie Hussain, Farookh Univ Technol Sydney Decis Syst & Eserv Intelligence Lab Ctr Quantum Computat & Intelligent Syst Sch SoftwareFac Engn & Informat Technol Broadway NSW 2007 Australia

ISBN: (纸本)9783642450051;9783642450044

Optimizing the scheduling of tasks in a distributed heterogeneous computing environment is a nonlinear multi-objective NP-hard problem which is playing an important role in optimizing cloud utilization and Quality of Service (QoS). In this paper, we develop a comprehensive multi-objective model for optimizing task scheduling to minimize task execution time, task transferring time, and task execution cost. However, the objective functions in this model are in conflict with one another. Considering this fact and the supremacy of particle swarm optimization (PSO) algorithm in speed and accuracy, we design a multi-objective algorithm based on multi-objective PSO (MOPSO) method to provide an optimal solution for the proposed model. To implement and evaluate the proposed model, we extend Jswarm package to multi-objective Jswarm (MO-Jswarm) package. We also extend Cloudsim toolkit applying MO-Jswarm as its task scheduling algorithm. MO-Jswarm in Cloudsim determines the optimal task arrangement among VMs according to MOPSO algorithm. The simulation results show that the proposed method has the ability to find optimal trade-off solutions for multi-objective task scheduling problems that represent the best possible compromises among the conflicting objectives, and significantly increases the QoS.

关键词： Cloud computing Task Scheduling multi-objective particle swarm optimization Jswarm Cloudsim

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IPMC Robust Nonlinear Tracking Control Design based on A multi-objective particle swarm optimization-based RRCF Approach

IPMC Robust Nonlinear Tracking Control Design based on A Mul...

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International conference on Advanced Mechatronic systems

作者： Liao, Wudai Yan, Tongbin Wang, Aihui Fu, Yiwen Zhongyuan Univ Technol Sch Elect & Informat Engn 41 Zhongyuan Rd Zhengzhou 450007 Peoples R China

ISBN: (纸本)9781467379960

In this paper, a robust nonlinear tracking control design for an ionic polymer metal composite (IPMC) is proposed by using a multi-objective particle swarm optimization-based robust right coprime factorization approach. Addressing the difficult in obtaining the PI control parameters (K-p, K-i) of the former proposed nonlinear robust tracking control system based on PI-based robust right coprime factorization approach, how to obtain the optimal control parameters (K-p, K-i) is investigated by using MATLAB system identification toolbox and multi-objective particle swarm optimization algorithm. That is, firstly, a new equivalent transfer function model of the robust stable control system of IPMC is identified. For the obtained transfer model, a multi-objective particle swarm algorithm optimization is used obtain the control parameters (K-p, K-i) of PI controller. Finally, the effectiveness of the proposed method system is confirmed by simulation results.

关键词： IPMC tracking control MATLAB system identification toolbox multi-objective particle swarm optimization

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A multi-objective particle swarm optimization Data Scheduling Algorithm for Peer-to-Peer Video Streaming 13

A Multi-objective Particle Swarm Optimization Data Schedulin...

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13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

作者： Liu, Pingshan Xiong, Xiaoyi Huang, Guimin Wen, Yimin Guilin Univ Elect Technol Business Sch Guangxi Key Lab Trusted Software Guilin Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Trusted Software Guilin Peoples R China

ISBN: (纸本)9781538621653

In P2P (Peer-to-Peer) video streaming systems using unstructured mesh, data scheduling is an important factor on system performance. An optimal data scheduling scheme should achieve two objectives ideally. The first objective is to optimize the perceived video quality of peers. The second objective is to maximize the network throughput, i.e., utilize the upload bandwidth of peers maximally. However, the optimized perceived video quality may not bring a maximized network throughput, and vice versa. In the paper, to better achieve the two objectives simultaneously, we formulate the data scheduling problem as a multi-objective optimization problem. To solve the multi-objective optimization problem, we propose a multi-objective particle swarm optimization data scheduling algorithm by encoding the peers' neighbors as the locations of the particles. Through simulations, we demonstrate the proposed algorithm outperforms other algorithms in terms of the perceived video quality and the utilization of peers' upload capacity.

关键词： Peer-to-Peer streaming data scheduling multi-objective optimization multi-objective particle swarm optimization

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multiple swarms multi-objective particle swarm optimization Based on Decomposition

Multiple Swarms Multi-objective Particle Swarm Optimization ...

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International Conference on Advanced in Control Engineering and Information Science (CEIS)

作者： Peng Hu Li Rong Cao Liang-lin Li Li-xian Jiujiang Univ Sch Informat Sci & Technol Jiujiang 332005 Peoples R China

particle swarm optimization is a very competitive swarm intelligence algorithm for multi-objective optimization problems, but because of it is easy to fall into local optimum solution, and the convergence and accuracy of Pareto solution set is not satisfactory. So we proposed a multi-swarm multi-objective particle swarm optimization based on decomposition (MOPSO_MS), in the algorithm each sub-swarm corresponding to a sub-problem which decomposed by multi-objective decomposition method, and we constructed a new updates strategy for the velocity. Finally, through simulation experiments and compare with the state-of-the-art multi-objective particle swarm algorithm on ZDT test function proved the convergence and the accuracy of the algorithm. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of [CEIS 2011]

关键词： multi-objective particle swarm optimization multi-swarm particle swarm optimization multi-objective decomposition Performance indicator

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optimization of Residential PV and Water Heating System's Configration and Operation Using multi-objective particle swarm optimization

Optimization of Residential PV and Water Heating System's Co...

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IEEE Power-and-Energy-Society Innovative Smart Grid Technologies Conference (ISGT)

作者： Yoshida, Yusuke Ueda, Yuzuru Tokyo Univ Sci Katushika Ku 6-3-1 Niijuku Tokyo 1258585 Japan

ISBN: (纸本)9781509051670

Massive PV introduction induces difficult power supply-demand balance adjustment. As one of solutions, a PV system and a Solar Hot-Water Supply System which is a hybrid system of a Solar Thermal Water Heater and a Heat Pump Water Heater is combined. In optimization of these configuration, it is necessary to consider the optimization of multiple objectives such as cost and energy efficiency. However, it is difficult to satisfy these objectives simultaneously since these are trade-offs. In this paper, we conducted to find pareto optimal solutions by multi-objective particle swarm optimization which is a kind of an evolutionary computing. To facilitate the solution search within the constraints conditions, we gave a flag to the excess and run-out of the heat storage amount. Thereby, we could find quasi-optimal solutions according to each objective, and conducted a search twice for solutions by multi-objective particle swarm optimization with fixed configuration to find better solutions with respect to operation. As a result, the evaluation values of all objective functions are improved.

关键词： multi-objective particle swarm optimization photovoltaic solar thermal water heater heat pump water heater

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A multi-objective particle swarm optimization BASED THRESHOLD APPROACH FOR SKIN COLOR DETECTION 12

A MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATION BASED THRESHOL...

引用

International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Luh, Guan-Chun Tatung Univ Dept Mech Engn Taipei 104 Taiwan

ISBN: (纸本)9781479902606

Skin detection plays an important role in a wide range of image processing applications such as face detection, tracking and recognition. Skin color is often considered to be a useful and discriminating image feature for facial area since it provides computationally effective yet, robust to variation in scale, orientation and partial occlusion. One of the simplest and common approaches is to identify a fixed decision boundary for different color space. Single or multiple threshold values are characterized for each color space components. Consequently an image pixel value falling within these predefined range(s) is classified as a skin pixel. In this paper, multi-objective particle swarm optimization is employed to determine the optimal threshhold ranges of each components in RGB-CbCrCg color spaces. The performance of the scheme was evaluated employing the ECU face and skin database.

关键词： Skin detection threshold multi-objective particle swarm optimization ECU face and skin database

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multi-power Quality Controller Coordination Control Based on multi-objective particle swarm optimization 2

Multi-power Quality Controller Coordination Control Based on...

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2nd IEEE International Conference on Electronics and Communication Engineering (ICECE)

作者： Shen Yu Yang Fan Wu Yaowen Zhong Peijun Yin Zhongdong Wang He State Grid Hubei Elect Power Co LTD Elect Power Res Inst Wuhan Peoples R China State Grid Hubei Elect Power Co LTD Wuhan Hubei Peoples R China Wuhan Univ Wuhan Peoples R China North China Elect Power Univ Beijing Peoples R China

ISBN: (数字)9781728147840

ISBN: (纸本)9781728147840

In a distribution network system with multiple power quality controllers, the control effect of each power quality controller is to ensure that the power quality of the access node can reach the expected goal, but due to the interaction effect between the power quality controllers, power quality controllers adopted in the local control of calculated to adjust instruction, unable to realize multiple nodes distribution system power quality at the same time to reach the expected aim. In this paper, for IEEE 33-node standard distribution network system, the nodes contraction method is firstly adopted to eliminate irrelevant nodes, the expression of interaction effect mechanism between power quality controllers is derived, and the relative gain system matrix (RGA) is introduced to quantitatively analyze the degree of interaction effect between controllers Then USES the master-slave control structure with central controller, selection of multi-objective particle swarm optimization (MOPSO) applied to the central controller of the coordinated control calculation, through multi-objective particle swarm optimization algorithm to get a group can make the distribution network system in the assessment of more nodes at the same time to achieve the desired effect of optimal solution set, and then by the central controller according to the optimal solution set distribution adjust instruction to the assessment of each node power quality controller is carried out. PSCAD simulation results show that the coordinated control effect is good.

关键词： interaction node contraction relative gain array multi-objective particle swarm optimization coordinated control

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Improving Generalization of Radial Basis Function Network with Adaptive multi-objective particle swarm optimization

Improving Generalization of Radial Basis Function Network wi...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Qasem, Sultan Noman Shamsuddin, Siti Mariyarn Hj Univ Teknol Malaysia Soft Comp Res Grp Fac Comp Sci & Informat Syst Skudai 81300 Johor Malaysia Univ Teknol Malaysia Soft Comp Res Grp K Econ Res Alliance Skudai 81300 Johor Malaysia

ISBN: (纸本)9781424427932

In this paper, an adaptive evolutionary multi-objective selection method of REF Networks structure is discussed. The candidates of REF Network structures are encoded into particles in particle swarm optimization (PSO). These particles evolve toward Pareto-optimal front defined by several objective functions with model accuracy and complexity. The problem of unsupervised and supervised learning is discussed with Adaptive multi-objective PSO (AMOPSO). This study suggests an approach of REF Network training through simultaneous optimization of architectures and weights with Adaptive PSO-based multi-objective algorithm. Our goal is to determine whether Adaptive multi-objective PSO can train REF Networks, and the performance is validated on accuracy and complexity. The experiments are conducted on two benchmark datasets obtained from the machine learning repository. The results show that our proposed method provides an effective means for training REF Networks that is competitive with PSO-based multi-objective algorithm.

关键词： Radial basis function network Adaptive multi-objective particle swarm optimization multi-objective particle swarm optimization

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Efficient Task Scheduling multi-objective particle swarm optimization in Cloud Computing 41

Efficient Task Scheduling Multi-Objective Particle Swarm Opt...

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41st IEEE Conference on Local Computer Networks (LCN)

作者： Alkayal, Entisar S. Jennings, Nicholas R. Abulkhair, Maysoon F. King Abdulaziz Univ Fac Comp & Informat Technol Jeddah Saudi Arabia Imperial Coll Comp Elect & Elect Engn London England

ISBN: (纸本)9781509023479

Task scheduling in data centers is a complex task due to their evolution in size, complexity, and performance. At the same time, customers' requirements have become more sophisticated in terms of execution time and throughput. Against this background, this work presents a new model of resource allocation that optimizes task scheduling using a multi-objective optimization (MOO) and particle swarm optimization (PSO) algorithm. In more detail, we develop a novel multi-objective PSO (MOPSO) algorithm, based on a new ranking strategy. The main insight of this algorithm is that the tasks are scheduled to the virtual machines to minimize waiting time and maximize system throughput. The algorithm leads to a reduction in execution time of 20%, a reduction the waiting time of 30%, and shows improvements of up to 40% in throughput compared to the current state of the art.

关键词： multi-objective particle swarm optimization cloud computing task scheduling ranking strategy

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Planning and Impact Evaluation of Distributed Generators in Indian Context using multi-objective particle swarm optimization

Planning and Impact Evaluation of Distributed Generators in ...

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General Meeting of the IEEE-Power-and-Energy-Society (PES)

作者： Jain, Naveen Singh, S. N. Srivastava, S. C. Indian Inst Technol Dept Elect Engn Kanpur 208016 Uttar Pradesh India

ISBN: (纸本)9781457710018

The placement and sizing of Distributed Generators (DGs) can be formulated as a nonlinear optimization problem to maximize the benefits from its placement, while minimizing its size. This paper proposes a voltage stability index based method for the DG placement and a meta-heuristic technique based on multi-objective particle swarm optimization (MOPSO) to provide Pareto optimal solutions. The MOPSO minimizes the DG size as well as the weighted sum of multiple system indices, reflecting the impact of DGs on the improvement of the system performance. The Pareto-optimal solutions present the possible trade-off between the multi-objective index and the DG size. The studies have been carried out on a 30-bus test system and a 41-bus Indian distribution system. The impacts of the DG placement on the system voltage profile, line loss, environment and cost of generation have also been investigated.

关键词： Distributed generation multi-objective particle swarm optimization radial distribution system

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