This paper presents the recent developments in hierarchical genetic algorithms (HGAs) to speed up the optimization of aerodynamic shapes. It first introduces HGAs, a particular instance of parallel GAs based on the no...
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This paper presents the recent developments in hierarchical genetic algorithms (HGAs) to speed up the optimization of aerodynamic shapes. It first introduces HGAs, a particular instance of parallel GAs based on the notion of interconnected sub-populations evolving independently. Previous studies have shown the advantages of introducing a multi-layered hierarchical topology in parallel GAs. Such a topology allows the use of multiple models for optimization problems, and shows that it is possible to mix fast low-fidelity models for exploration and expensive high-fidelity models for exploitation. Finally, a new class of multi-objective optimizers mixing HGAs and Nash Game Theory is defined. These methods are tested for solving design optimization problems in aerodynamics. A parallel version of this approach running a cluster of PCs demonstrate the convergence speed up on an inverse nozzle problem and a high-lift problem for a multiple element airfoil. (C) 2002 Published by Elsevier Science B.V.
In this paper a new model of a Multi-Objective hierarchicalgenetic Algorithm (MOHGA) based on the Micro genetic Algorithm (μGA) approach for Modular Neural Networks (MNNs) optimization is proposed. The proposed meth...
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ISBN:
(纸本)9781479904532
In this paper a new model of a Multi-Objective hierarchicalgenetic Algorithm (MOHGA) based on the Micro genetic Algorithm (μGA) approach for Modular Neural Networks (MNNs) optimization is proposed. The proposed method can divide the data automatically into granules or sub modules, and chooses which data are for the training and which are for the testing phase. The proposed Multi-Objective genetic Algorithm is responsible for determining the number of granules or sub modules and the percentage of data for training that can allow to have better results. The proposed method was applied to human recognition and its applicability with good results is shown, although the proposed method can be used in other applications such as time series prediction and classification.
The ease of use and re-configuration in a wireless network has played a key role in their widespread growth. The node deployment problem deals with an optimal placement strategy of the wireless nodes. This paper model...
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The ease of use and re-configuration in a wireless network has played a key role in their widespread growth. The node deployment problem deals with an optimal placement strategy of the wireless nodes. This paper models a wireless sensor network, consisting of a number of nodes, and a unique sink to which all the information is transmitted using the shortest connecting path. Traditionally the systems have used geneticalgorithms for optimal placement of the nodes that usually fail to give results in problems employing large numbers of nodes or higher areas to be covered. This paper proposes a hybrid genetic Programming (GP) and genetic Algorithm (GA) for solving the problem. While the GP optimizes the deployment structure, the GA is used for actual node placement as per the GP optimized structure. The GA serves as a slave and GP serves as master in this hierarchical implementation. The algorithm optimizes total coverage area, energy utilization, lifetime of the network, and the number of nodes deployed. Experimental results show that the algorithm could place the sensor nodes in a variety of scenarios. The placement was found to be better than random placement strategy as well as the genetic Algorithm placement strategy.
In this paper, a HGA fuzzy supervisory PI controller using hierarchical genetic algorithms is developed and implemented for controlling the top and bottom product quality of a nonlinear, multi-input multi-output binar...
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ISBN:
(纸本)0780393538
In this paper, a HGA fuzzy supervisory PI controller using hierarchical genetic algorithms is developed and implemented for controlling the top and bottom product quality of a nonlinear, multi-input multi-output binary distillation column when the disturbances enter the column in the form of the changes in feed flow rate. Two conventional PI controllers, one for the bottom product composition and another for the top product composition, are used together in a decentralized control scheme to per form dual composition control. hierarchical genetic algorithms are used to derive the optimal number and shape of membership functions and fuzzy rules of a fuzzy supervisory system that adapts the parameters of the PI controllers. The real-time implementation results show the effectiveness of the proposed method.
A kind of predictive control based on the neural network(NN) for nonlinear systems with time delay is *** off line NN model is obtained by using hierarchical genetic algorithms (HGA) to train a sequence data of inpu...
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A kind of predictive control based on the neural network(NN) for nonlinear systems with time delay is *** off line NN model is obtained by using hierarchical genetic algorithms (HGA) to train a sequence data of input and *** predictions are obtained by recursively mapping the NN *** error rectification term is introduced into a performance function that is directly optimized while on line control so that it overcomes influences of the mismatched model and disturbances,*** show the system has good dynamic responses and robustness.
In consideration of the difficulty in online measuring the component content in rare earth extraction separation production process, the soft-sensor method based on the radial basis function (RBF) neural networks is p...
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ISBN:
(纸本)1424403316
In consideration of the difficulty in online measuring the component content in rare earth extraction separation production process, the soft-sensor method based on the radial basis function (RBF) neural networks is proposed to measure the rare earth component content. The parameters of soft-sensor are optimized by the hierarchical genetic algorithms. In addition, application experiment research of this proposed method is carried out in the rare earth separation production process of a corporation. The results show that this method is effective and can realize online measuring for the component content of rare earth in the countercurrent extraction.
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