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ASME 7th Annual Dynamic Systems and Control conference

作者： Zhang, Haopeng Hui, Qing Texas Tech Univ Dept Mech Engn Lubbock TX 79409 USA

ISBN: (纸本)9780791846209

Model predictive control (MPC) is a heuristic control strategy to find a consequence of best controllers during each finite-horizon regarding to certain performance functions of a dynamic system. MPC involves two main operations: estimation and optimization. Due to high complexity of the performance functions, such as, nonlinear, non-convex, large-scale objective functions, the optimization algorithms for MPC must be capable of handling those problems with both computational efficiency and accuracy. Multiagent coordination optimization (MCO) is a recently developed heuristic algorithm by embedding multiagent coordination into swarm intelligence to accelerate the searching process for the optimal solution in the particle swarm optimization (PSO) algorithm. With only some elementary operations, the MCO algorithm can obtain the best solution extremely fast, which is especially necessary to solve the online optimization problems in MPC. Therefore, in this paper, we propose an MCO based MPC strategy to enhance the performance of the MPC controllers when addressing non-convex large-scale nonlinear problems. Moreover, as an application, the network resource balanced allocation problem is numerically illustrated by the MCO based MPC strategy.

关键词： optimization Predictive control Resource allocation

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Evolutionary algorithm-based multi-objective task scheduling optimization model in cloud environments

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WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS 2015年第6期18卷 1737-1757页

作者： Ramezani, Fahimeh Lu, Jie Taheri, Javid Hussain, Farookh Khadeer Univ Technol Sydney Decis Support & E Serv Intelligence Lab Ctr Quantum Computat & Intelligent Syst Sch SoftwareFac Engn & Informat Technol Sydney NSW 2007 Australia Univ Sydney Sch Informat Technol Ctr Distributed & High Performance Comp Sydney NSW 2006 Australia

Optimizing task scheduling in a distributed heterogeneous computing environment, which is a nonlinear multi-objective NP-hard problem, plays a critical role in decreasing service response time and cost, and boosting Quality of Service (QoS). This paper, considers four conflicting objectives, namely minimizing task transfer time, task execution cost, power consumption, and task queue length, to develop a comprehensive multi-objective optimization model for task scheduling. This model reduces costs from both the customer and provider perspectives by considering execution and power cost. We evaluate our model by applying two multi-objective evolutionary algorithms, namely Multi-Objective Particle Swarm optimization (MOPSO) and Multi-Objective Genetic Algorithm (MOGA). To implement the proposed model, we extend the Cloudsim toolkit by using MOPSO and MOGA as its task scheduling algorithms which determine the optimal task arrangement among VMs. The simulation results show that the proposed multi-objective model finds optimal trade-off solutions amongst the four conflicting objectives, which significantly reduces the job response time and makespan. This model not only increases QoS but also decreases the cost to providers. From our experimentation results, we find that MOPSO is a faster and more accurate evolutionary algorithm than MOGA for solving such problems.

关键词： Cloud computing Task scheduling Multi-objective particle swarm optimization Multi-objective genetic algorithm Jswarm Cloudsim

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Parallel Dynamic Step Size Sphere-Gap Transferring Algorithm for Solving Conditional nonlinear Optimal Perturbation 17

Parallel Dynamic Step Size Sphere-Gap Transferring Algorithm...

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2015 IEEE 17th International conference on high performance Computing and Communications (HPCC)

作者： Yuan, Shijin Yan, Jinghao Mu, Bin Li, Hongyu Tongji Univ Sch Software Engn Shanghai Peoples R China

ISBN: (纸本)9781479989379

Intelligent algorithms have been extensively applied in scientific computing. Recently, some researchers apply intelligent algorithms to solve conditional nonlinear optimal perturbation (CNOP) which is proposed to study the predictability of numerical weather and climate prediction. The difficulty of solving CNOP using the intelligent algorithm is the high dimensionality of complex numerical models. Therefore, previous researches either are just tested in ideal models or have low time efficiency in complex numerical models which limited the application of CNOP. In this paper, we proposed a parallel dynamic step size sphere-gap transferring algorithm (DSGT) to solve CNOP in complex numerical models. A dynamic step size factor is also designed to speed up convergence of sphere-gap transferring algorithm. Through the singular value decomposition, the original problem is reduced into a low-dimensional space to hunt the coordinate of the optimal CNOP with the DSGT algorithm. Moreover, in order to accelerate the computation speed, we parallelize the DSGT method with MPI technology. To demonstrate the validity, the proposed method has been studied in the Zebiak-Cane model to solve the CNOP. Experimental results prove that the proposed method can efficiently and stably obtain a satisfactory CNOP, and the parallel version can reach the speedup of 7.18 times with 10 cores.

关键词： sphere-gap transferring algorithm CNOP parallel Zebiak-Cane mode

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A Hybrid Method Based on Conjugate Gradient Trained Neural Network and Differential Evolution for Non Linear systems Identification

A Hybrid Method Based on Conjugate Gradient Trained Neural N...

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1st International conference on Electrical Engineering and software Applications (ICEESA)

作者： Ibtissem, Chiha Nouredine, Liouane Natl Sch Engineers Monastir ENIM Lab Automat Image & Signal Proc ATSI Monastir Tunisia

ISBN: (纸本)9781467363020;9781467363006

A hybrid method based on Differential Evolution and Neural Network training algorithms is presented in this paper for improving the performance of neural network in the non linear system identification. For this purpose, the local optimization algorithm of conjugate gradients (CG) is combined with the differential evolution algorithm (DE), which is a population-based stochastic global search method, to yield a computationally efficient algorithm for training multilayer perceptron networks for nonlinear system identification. After, a series of simulation studies of our method on the different nonlinear systems it has been confirmed that the proposed CG+DE algorithm has yielded better identification results in terms of time of convergence and less identification error.

关键词： Differential Evolution Conjugate Gradient Neural Network nonlinear system identification

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Evaluation of Wind Energy Parameter optimization of A DFIG Controller Based on Cultural algorithms 7

Evaluation of Wind Energy Parameter Optimization of A DFIG C...

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7th IEEE International conference on Communication and Signal Processing (IEEE ICCSP)

作者： Veeramani, C. Williams, Joseph Prabhakar Ramadevi, Punem Sri Indu Coll Engn & Technol Dept EEE Hyderabad India

ISBN: (纸本)9781538635216

In a wind energy conversion system (WECS), the characteristics are nonlinear and capturing maximum available power from the wind is a demanding task. The challenges in the WECS can be enhanced with optimal controller parameters of a doubly-fed induction generator (DFIG) to attain appreciable efficiency and improved dynamic response of the system. In this article, two research scenarios of wind dynamics based on the gust changes in the WECS with respect to voltage, current, power, rotor speed, and pitch angle have been analyzed. To explore the superiority of the proposed cultural algorithm-based controller optimization, its performance was compared with that of other well-established optimization techniques such as genetic algorithm and particle swarm optimization. The simulations were carried out in MATLAB software with appreciable system efficiency and improved dynamic response of the system. This proposed technique can also be extended for other complicated power system optimization problems.

关键词： Wind energy conversion system Cultural algorithms Doubly-fed induction generator

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A Comparison of Different optimization algorithms for HW/SW Partitioning Using a high-performance Cluster 18

A Comparison of Different Optimization Algorithms for HW/SW ...

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18th ACS/IEEE International conference on Computer Systems and Applications (AICCSA)

作者： Rahamneh, Samah Fong, Alvis Sawalha, Lina Univ Jordan Comp Engn Dept Amman Jordan Western Michigan Univ Comp Sci Dept Kalamazoo MI 49008 USA Western Michigan Univ Comp & Elect Engn Dept Kalamazoo MI 49008 USA

ISBN: (纸本)9781665409698

Hardware/software (HW/SW) co-design exploits the synergy between software and hardware to fulfill system design constraints. System designers have utilized diverse optimization algorithms to set boundaries between software and hardware. Discrete Particle Swarm optimization (DPSO) and Genetic Algorithm (GA) are efficient meta-heuristic algorithms for HW/SW partitioning. However, these algorithms might suffer from premature convergence. Moreover, the accuracy and the speed of convergence of PSO depend on control parameters, which might vary among different applications. In this work, we extended DPSO and GA with distributed greedy local search mechanisms that improve the performance of DPSO and GA. We also tuned the acceleration parameters of DPSO using a neural network. We partitioned real-world applications implemented using OpenCL nd Intel's Hardware Research Acceleration Program (HARP) infrastructure. The results show that DPSO with tuned parameters improves the accuracy of DPSO by up to 62.8%, and its execution time by up to 29%. On the other hand, local search-based DPSO improves the accuracy of DPSO by up to 55.4%, and the local search-based GA improves the accuracy of GA by up to 82.6%. However, the local search-based technique increases the execution time of the algorithm.

关键词： HW/SW partitioning particle swarm optimization genetic algorithm machine learning CPU-FPGA platforms

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Noise Cancellation in Cognitive Radio Systems: A performance Comparison of Evolutionary algorithms 7

Noise Cancellation in Cognitive Radio Systems: A Performance...

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7th IEEE Annual Computing and Communication Workshop and conference (IEEE CCWC)

作者： Quadri, Adnan Manesh, Mohsen Riahi Kaabouch, Naima Univ North Dakota Dept Elect Engn Grand Forks ND 58203 USA

ISBN: (纸本)9781509042289

Noise cancellation is one of the important signal processing functions of any communication system, as noise affects data integrity. In existing systems, traditional filters are used to cancel the noise from the received signals. These filters use fixed hardware which is capable of filtering specific frequency or a range of frequencies. However, next generation communication technologies, such as cognitive radio, will require the use of adaptive filters that can dynamically reconfigure their filtering parameters for any frequency. To this end, a few noise cancellation techniques have been proposed, including least mean squares (LMS) and its variants. However, these algorithms are susceptible to non-linear noise and fail to locate the global optimum solution for de-noising. In this paper, we investigate the efficiency of two global search optimization based algorithms, genetic algorithm and particle swarm optimization in performing noise cancellation in cognitive radio systems. These algorithms are implemented and their performances are compared to that of LMS using bit error rate and mean square error as performance evaluation metrics. Simulations are performed with additive white Gaussian noise and random nonlinear noise. Results indicate that GA and PSO perform better than LMS for the case of AWGN corrupted signal but for non-linear random noise PSO outperforms the other two algorithms.

关键词： Noise Cancellation Adaptive Filters Evolutionary algorithms Gradient-descent algorithms Cognitive Radio Particle Swarm optimization Genetic Algorithm Least Mean Square software Defined Radio

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Apply modified method of nonlinear optimization to improve localization accuracy in WSN

Apply modified method of nonlinear optimization to improve l...

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5th International conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2009

作者： Haiqing, Jiang Renzhi, Cao Xingfu, Wang Department of Computer Science and Technology USTC Hefei 230027 China

ISBN: (纸本)9781424436934

As is known, location information is playing an important role in most application of WSN, and thus increasing the location accuracy is crucial to this application. However, most of the location refinement algorithms used at present cannot meet the requirement of WSN very well. The linear optimization can achieve small calculating amount at the cost of reducing the positioning accuracy. The traditional unconstrained nonlinear optimization has a better performance in accuracy but always demands large calculating amount. Basing on the principle of unconstrained nonlinear optimization and combining with the characteristics of WSN, this paper proposes two improved novel refinement algorithms: NSSD and MNSQN. Simulation results show that the algorithms proposed in the paper are efficient to relieve the contradiction between calculating amount and localization accuracy by improving the traditional algorithms. The two optimization methods have several advantages: high localization accuracy, relatively low calculating amount, without requiring extra communicational cost, etc. ©2009 IEEE.

关键词： Location

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Distributed coordination in optimization algorithms

Distributed coordination in optimization algorithms

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high performance Computing and Networking Europe 1997 conference

作者： Di Martino, B Mazzeo, A Mazzocca, N Russo, S Univ Naples 2 Dipartimento Sci Informazione Naples Italy Univ Vienna Inst Software Technol & Parallel Syst A-1010 Vienna Austria Univ Naples Federico II Dipartimento Informat & Sistemist Naples Italy

ISBN: (纸本)3540628983

In this paper we present a parallel algorithm for the solution of discrete optimization problems, which is a typical example of highly irregularly structured problems. The Processor Farm model, well suited for this class of problems, has been modified to eliminate the presence of the coordinator, which represents a bottleneck for the parallel computation.

关键词： optimization

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Improved high-dimensional regression models with matrix approximations applied to the comparative case studies with support vector machines

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optimization METHODS & software 2022年第5期37卷 1912-1929页

作者： Roozbeh, Mahdi Babaie-Kafaki, Saman Aminifard, Zohre Semnan Univ Fac Math Stat & Comp Sci Semnan 35195363 Semnan Province Iran

Nowadays, high-dimensional data appear in many practical applications such as biosciences. In the regression analysis literature, the well-known ordinary least-squares estimation may be misleading when the full ranking of the design matrix is missed. As a popular issue, outliers may corrupt normal distribution of the residuals. Thus, since not being sensitive to the outlying data points, robust estimators are frequently applied in confrontation with the issue. Ill-conditioning in high-dimensional data is another common problem in modern regression analysis under which applying the least-squares estimator is hardly possible. So, it is necessary to deal with estimation methods to tackle these problems. As known, a successful approach for high-dimension cases is the penalized scheme with the aim of obtaining a subset of effective explanatory variables that predict the response as the best, while setting the other parameters to zero. Here, we develop several penalized mixed-integer nonlinear programming models to be used in high-dimension regression analysis. The given matrix approximations have simple structures, decreasing computational cost of the models. Moreover, the models are effectively solvable by metaheuristic algorithms. Numerical tests are made to shed light on performance of the proposed methods on simulated and real world high-dimensional data sets.

关键词： Robustness and sensitivity analysis regression analysis penalized method rank-one update diagonal approximation metaheuristic algorithm

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