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Migration selection of strategies for parallel genetic algorithms: implementation on networks on chips

INTERNATIONAL JOURNAL OF ELECTRONICS 2010年第10期97卷 1227-1240页

作者： Mourelle, L. Ferreira, R. E. Nedjah, N. Univ Estado Rio De Janeiro Dept Syst Engn & Computat Rio De Janeiro Brazil Univ Estado Rio De Janeiro Ctr Informat Rio De Janeiro Brazil Univ Estado Rio De Janeiro Dept Elect Engn & Telecommun Rio De Janeiro Brazil

The aim of the work described in this article is to investigate migration strategies for the execution of parallel genetic algorithms in a multi-processor system-on-chip (MPSoC). Some multimedia and internet applications for wireless communications are using genetic algorithms and can benefit from the advantages provided by parallel processing on MPSoCs. In order to run such algorithms, we use a network-on-chip platform, which provides the interconnection network required for the communication between processors. Two migration strategies are employed in order to analyse the speedup and efficiency each one can provide, considering the communication costs they require.

关键词： parallel genetic algorithm multi-processor system-on-chip network-on-chip migration topologies

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Acquisition and reconstruction of scattering characteristics of thermal insulation ceramics

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INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER 2025年 236卷

作者： Shi, Yu Xia, Xin-Lin Sun, Chuang Chen, Xue Harbin Inst Technol HIT Sch Energy Sci & Engn 92 West Dazhi St Harbin 150001 Peoples R China

Accurately obtaining the radiation properties of insulating ceramic materials is essential for engineering applications. This article obtained the bidirectional transmittance and reflectance of thermal insulation ceramics, and introduced a new scattering phase function to establish a radiation transfer model based on Monte Carlo method. Combined with parallel genetic algorithm inversion, radiation properties such as extinction coefficient, scattering albedo, and scattering phase function are obtained. Firstly, the experimental optical path is simulated and analyzed, which has little effect on the measurement results due to slight deflection of strong extinction material samples and detectors. For the measurement of bidirectional transmittance, a larger spot radius and detector radius will increase the measurement bidirectional transmittance. Secondly, through parallel genetic algorithm inversion, >5 measurement points are required to obtain their radiative properties, however, the radiation properties of backscattering materials cannot be precisely obtained using bidirectional transmittance for inversion. The required inversion accuracy can be achieved when the bidirectional transmittance and reflectance ratio measurement angle step is <4 degrees . Finally, this study determined the radiation properties of ceramic insulating materials that show little wavelength variation, their spectral extinction coefficients are above 9000m(-1), and spectral scattering albedo are greater than 0.9. It is difficult to characterize scattering features using isotropic scattering phase functions because materials have both forward and backward scattering characteristics. The scattering characteristics of insulation ceramics described using the newly proposed scattering phase function have higher accuracy.

关键词： Insulation ceramics Radiation transfer parallel genetic algorithm Extinction coefficient Scattering phase functions

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Improvement of remote monitoring on water quality in a subtropical reservoir by incorporating grammatical evolution with parallel genetic algorithms into satellite imagery

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WATER RESEARCH 2008年第1-2期42卷 296-306页

作者： Chen, Li Tan, Chih-Hung Kao, Shuh-Ji Wang, Tai-Sheng Chung Hua Univ Dept Civil Engn & Engn Informat Hsinchu 30067 Taiwan Agr Engn Res Ctr Informat Div Tao Yuan 32061 Taiwan Acad Sinica Res Ctr Environm Change Taipei 11529 Taiwan

parallel GEGA was constructed by incorporating grammatical evolution (GE) into the parallel genetic algorithm (GA) to improve reservoir water quality monitoring based on remote sensing images. A cruise was conducted to ground-truth chlorophyll-a (Chl-a) concentration longitudinally along the Feitsui Reservoir, the primary water supply for Taipei City in Taiwan. Empirical functions with multiple spectral parameters from the Landsat 7 Enhanced Thematic Mapper (ETM+) data were constructed. The GE, an evolutionary automatic programming type system, automatically discovers complex nonlinear mathematical relationships among observed Chl-a concentrations and remote-sensed imageries. A GA was used afterward with GE to optimize the appropriate function type. Various parallel subpopulations were processed to enhance search efficiency during the optimization procedure with GA. Compared with a traditional linear multiple regression (LMR), the performance of parallel GEGA was found to be better than that of the traditional LMR model with lower estimating errors. (C) 2007 Elsevier Ltd. All rights reserved.

关键词： grammatical evolution parallel genetic algorithm water quality monitoring chlorophyll-a remote-sensed imagery

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Damage Detection with parallel genetic algorithms and Operational Modes

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STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2010年第6期9卷 481-496页

作者： Meruane, V. Heylen, W. Univ Chile Dept Mech Engn Santiago Chile Katholieke Univ Leuven Dept Mech Engn B-3001 Heverlee Belgium

The detection of damage with model-based methods is a constrained nonlinear optimization problem. Conventional optimization approaches usually lead to local minima. Furthermore, they are highly sensitive to experimental noise or numerical errors. genetic algorithms (GAs) provide an attractive alternative since they can potentially explore the entire solution space and reach the global optimum. However, GAs are inherently slow when they work with complicated or time consuming objective functions. To overcome this problem parallel GAs are proposed, and they are particularly easy to implement and provide a superior numerical performance. In this study, a real-coded parallel GA is implemented to detect structural damage. The objective function is based on operational modal data;it considers the initial errors in the numerical model. False damage detection is avoided by using damage penalization. The algorithm is verified with two experimental cases. First, a test structure of an airplane subjected to three increasing levels of damage. Second, a multiple cracked reinforced concrete beam that is subjected to a nonsymmetrical increasing static load to introduce cracks. In both cases, the detected damage has a good correspondence with the experimental damage.

关键词： damage detection parallel genetic algorithm operational modes

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Probabilistic ensemble Fuzzy ARTMAP optimization using hierarchical parallel genetic algorithms

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NEURAL COMPUTING & APPLICATIONS 2015年第2期26卷 263-276页

作者： Loo, Chu Kiong Liew, Wei Shiung Seera, Manjeevan Lim, Einly Univ Malaya Fac Comp Sci & Informat Technol Kuala Lumpur Malaysia Univ Malaya Fac Biomed Engn Kuala Lumpur Malaysia

In this study, a comprehensive methodology for overcoming the design problem of the Fuzzy ARTMAP neural network is proposed. The issues addressed are the sequence of training data for supervised learning and optimum parameter tuning for parameters such as baseline vigilance. A genetic algorithm search heuristic was chosen to solve this multi-objective optimization problem. To further augment the ARTMAP's pattern classification ability, multiple ARTMAPs were optimized via genetic algorithm and assembled into a classifier ensemble. An optimal ensemble was realized by the inter-classifier diversity of its constituents. This was achieved by mitigating convergence in the genetic algorithms by employing a hierarchical parallel architecture. The best-performing classifiers were then combined in an ensemble, using probabilistic voting for decision combination. This study also integrated the disparate methods to operate within a single framework, which is the proposed novel method for creating an optimum classifier ensemble configuration with minimum user intervention. The methodology was benchmarked using popular data sets from UCI machine learning repository.

关键词： Ensembles Fuzzy ARTMAP Plurality voting parallel genetic algorithm Feature selection

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Structural damage assessment with antiresonances versus mode shapes using parallel genetic algorithms

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STRUCTURAL CONTROL & HEALTH MONITORING 2011年第8期18卷 825-839页

作者： Meruane, Viviana Heylen, Ward Katholieke Univ Leuven Dept Mech Engn Div Prod Engn Machine Design & Automat PMA B-3001 Heverlee Belgium Univ Chile Dept Mech Engn Santiago Chile

Antiresonances have become an attractive alternative in structural damage assessment. They can be identified easier and more accurately than mode shapes, and still providing the same information. Antiresonances are derived from point frequency response functions (FRFs) or from transfer FRFs. However, antiresonances from transfer FRFs are very sensitive to small structural changes, and the matching between numerical and experimental antiresonances is affected. This problem is solved if antiresonances from point FRFs are used. However, it implies an experimental procedure that differs from a common modal testing, which may become not practical or too expensive. This paper proposes a damage detection method able to deal with transfer antiresonances. The inverse problem is handled by a parallel genetic algorithm. In this case, a perfect match between the antiresonances is not required because the optimization is not gradient based. Moreover, the matching can change at each step and the optimization is not affected. The algorithm is verified with two experimental cases: an exhaust system of a car with a single fatigue crack and a tridimensional space frame structure with single and multiple damage scenarios. Results are compared with the ones obtained using mode shapes. Damage detected is consistent with the experimental damage in both cases. Copyright (C) 2010 John Wiley & Sons, Ltd.

关键词： damage assessment antiresonances parallel genetic algorithm damage penalization

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Volumetric segmentation of brain images using parallel genetic algorithms

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2002年第8期21卷 904-909页

作者： Fan, Y Jiang, TZ Evans, DJ Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100080 Peoples R China Nottingham Trent Univ Dept Comp & Math Nottingham NG1 4BU England

Active model-based segmentation has frequently been used in medical image processing with considerable success. Although the active model-based method was initially viewed as an optimization problem, most researchers implement it as a partial differential equation solution. The advantages and disadvantages of the active model-based method are distinct: speed and stability. To improve its performance, a parallel genetic algorithm-based active model method is proposed and applied to segment the lateral ventricles from magnetic resonance brain images. First, an objective function is defined. Then one instance surface was extracted using the finite-difference method-based active model and used to initialize the first generation of a parallel genetic algorithm. Finally, the parallel genetic algorithm is employed to refine the result. We demonstrate that the method successfully overcomes numerical instability and is capable of generating an accurate and robust anatomic descriptor for complex objects in the human brain, such as the lateral ventricles.

关键词： active models global optimization image segmentation parallel genetic algorithm

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APPLICATION OF parallel genetic algorithmS TO GENERATION EXPANSION PLANNING USING parallel PROCESSORS

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ELECTRICAL ENGINEERING IN JAPAN 1995年第6期115卷 71-81页

作者： FUKUYAMA, Y UEKI, Y Fuji Electric Co. R&D Ltd

This paper presents an application of parallel genetic algorithms (PGA) to the optimal long-range generation expansion planning. The problem can be formulated as a combinatorial optimization problem that determines the order of introduced generation units at each interval of the year. The proposed method considers introduced power limits of each technology, maximum loads at each interval, and load duration curves at each interval. Appropriate string representation for the problem is presented. Binary and decimal coding and three selection methods are compared. The method is developed on a transputer that is one of the parallel processors. The feasibility of the proposed method is demonstrated using a typical expansion problem with four technologies and five intervals. The method is then compared with conventional dynamic programming and a simple genetic algorithm with promising results.

关键词： GENERATION EXPANSION PLANNING OPTIMAL GENERATION INTRODUCTION COMBINATORIAL OPTIMIZATION parallel genetic algorithm parallel PROCESSORS

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A One Stage Damage Detection Technique Using Spectral Density Analysis and parallel genetic algorithms

A One Stage Damage Detection Technique Using Spectral Densit...

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4th Asia-Pacific Workshop on Structural Health Monitoring

作者： Varmazyar, Maryam Haritos, Nicholas Kirley, Michael Peterson, Tim Univ Melbourne Dept Infrastruct Engn Melbourne Vic Australia Univ Melbourne Dept Comp & Informat Sci Melbourne Vic Australia

ISBN: (纸本)9783037857151

This paper describes a new global damage identification framework for the continuous/periodic monitoring of civil structures. In order to localize and estimate the severity of damage regions, a one-stage model-based Bayesian probabilistic damage detection approach is proposed. This method, which is based on the response power spectral density of the structure, enjoys the advantage of broadband frequency information and can be implemented on input-output as well as output-only damage identification studies. A parallel genetic algorithm is subsequently used to evolve the optimal model parameters introduced for different damage conditions. Given the complex search space and the need to perform multiple time-consuming objective function evaluations, a parallel meta-heuristic provides a robust optimization tool in this domain. It is shown that this approach is capable of detecting structural damage in both noisy and noise-free environments.

关键词： damage identification power spectral density parallel genetic algorithm severity Bayesian probabilistic approach

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Intelligent transport route planning using parallel genetic algorithms and MPI in high performance computing cluster

Intelligent transport route planning using parallel genetic ...

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15th International Conference on Advanced Computing and Communications

作者： Arunadevi, J. Johnsanjeevkumar, A. Sujatha, N. Thiagarajar Sch Management Madurai Tamil Nadu India Thiagarajar Coll Engn Madurai Tamil Nadu India Madurai Kamaraj Univ Madurai Tamil Nadu India

ISBN: (纸本)9780769530598

Network analysis in geospatial information system (GIS) provides strong decision support for users in searching optimal route, finding the nearest facility and determining the service area. Searching optimal path is an important advanced analysis function in GIS. In present GIS route finding modules, heuristic algorithms have been used to carry out its search strategy. Due to the lack of global sampling in the feasible solution space, these algorithms have considerable possibility of being trapped into local optima. This paper addresses the problem of selecting route to a given destination on an actual map under a static environment. The proposed solution uses a parallel genetic algorithm (PGA) implemented using High performance Cluster(HPC). A part of an arterial road is regarded as a virus. We generate a population of viruses in addition to a population of routes. A customized method based on a genetic algorithm has been proposed in this paper.

关键词： GIS SDSS parallel genetic algorithm route finding vehicle routing problem

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