In this paper, we examine the data replication problem in a particular grid delivery network (GDN). In this system, the data are divided into fixed size blocks which must be replicated on hosts to decrease the total d...
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In this paper, we examine the data replication problem in a particular grid delivery network (GDN). In this system, the data are divided into fixed size blocks which must be replicated on hosts to decrease the total download time. We propose a probabilistic model to optimize the average download time of requests based on the hosts availability and the document size distribution. The objective function induced by this model is a nonlinear integer problem. It can be solved in real values by Lagrangian optimization. We prove that in a particular case, this problem can be reduced to a knapsack problem. We propose approximation algorithms and validate them using simulations with varying characteristics.
In the past decade, the scientific community has become more interested in Near Earth Objects (NEOs) and the threat they pose to existence of life on this planet. The recent trend in NEO deflection technique research ...
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This paper reports the comparison study of particle swarm optimization (PSO) and evolutionary particle swarm optimization (EPSO) algorithms and their application to the optimal capacitor placement in radial power dist...
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This paper reports the comparison study of particle swarm optimization (PSO) and evolutionary particle swarm optimization (EPSO) algorithms and their application to the optimal capacitor placement in radial power distribution system. Using JAVA language, software programs have been developed with PSO and 2 variant EPSO algorithms. The comparison study is then carried-out on the various versions of EPSO and PSO algorithms to analyze the performance of each algorithm in solving the capacitor placement problem. A power distribution system from Melaka, Malaysia has been used in this study. The results clearly indicate that EPSO is superior to PSO in finding the optimal solution and handling more complex, nonlinear objective functions due to its self-adaptability. However, EPSO is more computationally intense, requiring more computational time per iteration.
In resent years, research has taken an interest in design of approximation algorithms due to the requirement of these algorithms for solving many problems of science and engineering like system modeling, identificatio...
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In resent years, research has taken an interest in design of approximation algorithms due to the requirement of these algorithms for solving many problems of science and engineering like system modeling, identification of plants, controller design, fault detection, computer security, prediction of data sets etc. The area of Artificial Immune System (AIS) is emerging as an active and attractive field involving models, techniques and applications of greater diversity. In this paper a new optimization algorithm based on AIS is developed. The proposed algorithm has been suitably applied to develop practical applications like design of a new model for efficient approximation of nonlinear functions and identification of nonlinear systems in noisy environments. Simulation study of few benchmark function approximation and system identification problems are carried out to show superior performance of the proposed model over the standard methods in terms of response matching, accuracy of identification and convergence speed achieved.
In this paper, we propose a new particle swarm optimization (PSO), which is based on successive optimization in its parameter space, in order to overcome the difficulty for applying PSO to complex and high dimensional...
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In this paper, we propose a new particle swarm optimization (PSO), which is based on successive optimization in its parameter space, in order to overcome the difficulty for applying PSO to complex and high dimensional nonlinearoptimization problems. The proposed PSO consists of two types of optimization procedures; optimization in its decision variable space and optimization in its parameter space. Some numerical simulations using 6 types of typical benchmark problems verify the performance of the proposed PSO.
While the demand for memory capacity and performance continues to increase, current DDR memory implementations start to encounter limitations. At high data rates of 533MT/s and above, it becomes increasingly difficult...
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ISBN:
(纸本)9780863419317
While the demand for memory capacity and performance continues to increase, current DDR memory implementations start to encounter limitations. At high data rates of 533MT/s and above, it becomes increasingly difficult to support different combinations of DDR raw card types on the same platform due to the possible variations in load. This paper outlines a method of maximizing the DDR bus performance by utilizing hardware circuitry in the memory controller in connection with softwarealgorithms to adjust the DDR transaction timing relationships based on the populated memory configuration. The algorithm also compensate for the effects of ageing over the lifetime of the part. The techniques used in this work are related to DDR-2 but could also be applicable to DDR-3 and future technologies.
A preferable value for parameters proved to be crucial in enhancing the performance and efficiency of particle swarm optimization (PSO) algorithm. To provide good solution for reasonable choice of parameter values wit...
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A preferable value for parameters proved to be crucial in enhancing the performance and efficiency of particle swarm optimization (PSO) algorithm. To provide good solution for reasonable choice of parameter values within fairly wide range for particle swarm optimization, this paper presents a novel parameter optimizing configuration strategy based on multi-order rhombus thought (MRT), which depends on the optimization function to adaptively configure the most suitable set of parameters. With the divergent-concentrate-redivigent-reconcentrate nature of MRT, parameters are gradually optimized by the rhombus thought process as feedback information of the evolutionary process. Compared with other main improved methods, the computation procedures of MRTPSO algorithm are discussed, and numerical experiments based on typical benchmarks are given to illustrate the better convergence characteristic and shorter executing time of MRTPSO algorithm.
In this paper, an optimization algorithm has been developed to provide robust designs for nonlinear beam-wave interaction of high-frequency traveling-wave tubes (TWTs). The algorithm utilizes the optimization method o...
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ISBN:
(纸本)9781424417155
In this paper, an optimization algorithm has been developed to provide robust designs for nonlinear beam-wave interaction of high-frequency traveling-wave tubes (TWTs). The algorithm utilizes the optimization method of hybrid genetic algorithm (Goldberg, 1990) in the TWT simulation code which is much like CHRISTINE-1D (Antonsen, 1998). By considering the effects of dimensional helix pitch variations during the optimization, the desired performance characteristics has been achieved.
This paper is a continuation of a previous work by the same authors concerning the use of automated high-level synthesis tools for obtaining high-performance FPGA implementations of industrial automation and control a...
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This paper is a continuation of a previous work by the same authors concerning the use of automated high-level synthesis tools for obtaining high-performance FPGA implementations of industrial automation and control algorithms coded as PLC programs. The proposed method is mainly targeting demanding applications requiring lots of numerical computations. high-level synthesis is based on powerful, commercial tools. Since most of these tools are not compatible with PLC development environments, custom translating software built by using standard compiler techniques, can be employed for converting PLC programs to a form that can be understood by the selected tools. Experimental results involving both fixed-point and floating point implementations of three well-known industrial control algorithms are presented.
Distributed data mining and in particular grid-enabled data mining has become an active area of research and development in recent years. As the amount of available digital electronic data is growing at an unprecedent...
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Distributed data mining and in particular grid-enabled data mining has become an active area of research and development in recent years. As the amount of available digital electronic data is growing at an unprecedented rate, it is necessary to provide general data mining algorithms that help to leverage grid capacity in supporting high-performance distributed computing for solving their data mining problem in a distributed way. In this paper, an optimal multi-strategy based hybrid distribution (MBHD) algorithm based on knowledge grid is proposed for performance improvement over current grid-based association rule mining algorithms. With the optimization polices based on auction model and timestamp mechanism, MBHD algorithm effectively solves the load imbalance problem in grid environment and decreases the communication overhead. The response time performance of MBHD algorithm with different numbers of hosts and minimum supports is analyzed by experiments. The numerical results show that MBHD is efficient and performs better than count distribution (CD) algorithm, intelligent data distribution (IDD) algorithm and hybrid distribution (HD) algorithm.
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