In this paper, we present a genetic algorithm optimization scheme based on a clustering method, and employ the optimum algorithm combined with the 3D-FDFD (3 dimension frequency domain finite difference) to optimize a...
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ISBN:
(纸本)078039433X
In this paper, we present a genetic algorithm optimization scheme based on a clustering method, and employ the optimum algorithm combined with the 3D-FDFD (3 dimension frequency domain finite difference) to optimize a design of multi-layer patch antennas. A resulting antenna exhibits a higher accuracy, speed of the modified genetic algorithm.
Population-based Incremental Learning is shown require very sensitive scaling of its learning rate. The learning rate must scale with the system size in a problem-dependent way. This is shown in two problems: the need...
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ISBN:
(纸本)0262025507
Population-based Incremental Learning is shown require very sensitive scaling of its learning rate. The learning rate must scale with the system size in a problem-dependent way. This is shown in two problems: the needle-in-a haystack, in which the learning rate must vanish exponentially in the system size, and in a smooth function in which the learning rate must vanish like the square root of the system size. Two methods are proposed for removing this sensitivity. A learning dynamics which obeys detailed balance is shown to give consistent performance over the entire range of learning rates. An analog of mutation is shown to require a learning rate which scales as the inverse system size, but is problem independent.
Many optimization problems, such as manufacturing process planning optimization, are difficult problems due to the large number of potential configurations (process sequences) and associated (process) parameters. In a...
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Many optimization problems, such as manufacturing process planning optimization, are difficult problems due to the large number of potential configurations (process sequences) and associated (process) parameters. In addition, the search space is highly discontinuous and multi-modal. This paper introduces an agent based optimization algorithm that combines stochastic optimization techniques with knowledge based search. The motivation is that such a merging takes advantage of the benefits of stochastic optimization and accelerates the search process using domain knowledge. The result of applying this algorithm to computerized manufacturing process models is presented.
In this paper, we approach the gate sizing problem in VLSI circuits in the context of increasing variability of process and circuit parameters as technology scales into the nanometer regime. We present a statistical s...
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ISBN:
(纸本)0769522319
In this paper, we approach the gate sizing problem in VLSI circuits in the context of increasing variability of process and circuit parameters as technology scales into the nanometer regime. We present a statistical sizing approach that takes into account randomness in gate delays by formulating a robust linear program that can be solved efficiently. We demonstrate the efficiency and computational tractability of the proposed algorithm on the various ISCAS'85 benchmark circuits. Across the benchmarks, compared to the deterministic approach, the power savings range from 23 - 30% for the same timing target and the yield level, the average power saving being 28%. The runtime is reasonable, ranging from a few seconds to around 10 mins, and grows linearly.
The Sequential Minimal optimization (SMO) algorithm is a popular algorithm used to solve the Support Vector Machine problem due to its efficiency and ease of implementation. We investigate applying extrapolation metho...
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ISBN:
(纸本)0780382439
The Sequential Minimal optimization (SMO) algorithm is a popular algorithm used to solve the Support Vector Machine problem due to its efficiency and ease of implementation. We investigate applying extrapolation methods to the SMO update method in order to increase the rate of convergence of this algorithm. We first show that the update method is Newtonian and that extrapolation ensures the update is norm reducing on the objective function. We also note that choosing the working set pair according to some partial order does result in slightly faster speedups in algorithm performance.
The effect of a repair of a complex system can usually be approximated by the following two types: minimal repair for which the system is restored to its functioning state with minimum effort, or perfect repair for wh...
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The effect of a repair of a complex system can usually be approximated by the following two types: minimal repair for which the system is restored to its functioning state with minimum effort, or perfect repair for which the system is replaced or repaired to a good-as-new state. When both types of repair are possible an important problem is to determine the repair policy;that is, the type of repair which should be carried out after a failure. In this paper, an optimal. Allocation problem is studied for a monotonic failure rate repairable system under some resource constraints. In the first model, the numbers of minimal & perfect repairs are fixed, and the optimal repair policy maximizing the expected system lifetime is studied. In the second model, the total amount of repair resource is fixed and the costs of each minimal & perfect repair are assumed to be known. The optimal allocation algorithm is derived in this case. Two numerical examples are shown to illustrate the procedures.
We review the space-mapping (SM) technique and the SM-based surrogate (modeling) concept and their applications in engineering design optimization. For the first time, we present a mathematical motivation and place SM...
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We review the space-mapping (SM) technique and the SM-based surrogate (modeling) concept and their applications in engineering design optimization. For the first time, we present a mathematical motivation and place SM into the context of classical optimization. The aim of SM is to achieve a satisfactory solution with a minimal number of computationally expensive "fine" model evaluations. SM procedures iteratively update and optimize surrogates based on a fast physically based "coarse" model. Proposed approaches to SM-based optimization include the original algorithm, the Broyden-based aggressive SM algorithm, various trust-region approaches, neural SM, and implicit SM. Parameter extraction is an essential SM subproblem. It is used to align the surrogate (enhanced coarse model) with the fine model. Different approaches to enhance uniqueness are suggested, including the recent gradient parameter-extraction approach. Novel physical illustrations are presented, including the cheese-cutting and wedge-cutting problems. Significant practical applications are reviewed.
This paper describes the model-based design and the experimental validation of a control system which suppresses the bouncing behavior of Compressed Natural Gas (CNG) fuel injectors. First a detailed model of the syst...
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This paper describes the model-based design and the experimental validation of a control system which suppresses the bouncing behavior of Compressed Natural Gas (CNG) fuel injectors. First a detailed model of the system is developed, including temperature and supply-voltage variation effects. Using an optical position sensor this model is experimentally validated in a second step. Based on this model a feed-forward controller is developed and tested which minimizes the bouncing energy of the system. Since in series applications position sensing would be too expensive to use, an observer-based iterative control algorithm is derived which uses coil current measurements instead of the position information to asymptotically suppress bouncing.
As high capacity all-optical networks and WDM technologies advance and merge together aggregating low-speed traffic streams onto high-speed wavelengths becomes more critical. Efficient aggregation techniques, known as...
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ISBN:
(纸本)0769522211
As high capacity all-optical networks and WDM technologies advance and merge together aggregating low-speed traffic streams onto high-speed wavelengths becomes more critical. Efficient aggregation techniques, known as traffic grooming, allow higher bandwidth utilization and can reduce request blocking probability. These algorithms can also result in lower network cost in terms of electronic switching. In this paper we focus on traffic grooming in WDM mesh networks with dynamic traffic patterns. We offer two new grooming concepts called lightpath dropping and lightpath extension. These concepts are based on an alternative node architecture in which incoming optical signals can be dropped at a node, while optically continuing to the next node. Based on these concepts, we develop several grooming algorithms and study them under various network objectives. We also compare their performance with previously proposed lightpath-based grooming algorithms. Through extensive simulation results we show that our proposed approaches lead to lower request blocking probability and lower average number of logical hops when the number of transceivers per node is limited.
The Software Radio concept is one of the emerging new technologies to result fast progress in the converging telecommunication and information system. This paper describes the structure of the Software Radio System, a...
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ISBN:
(纸本)9537044025
The Software Radio concept is one of the emerging new technologies to result fast progress in the converging telecommunication and information system. This paper describes the structure of the Software Radio System, and the role of optimization algorithms in Software Radio Environment. The article gives overview the principle of the resource management, the alternatives of optimization, the implementing of reconfigurable resource controller and the realizing of a test environment.
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