Parallel processing, neural networks and genetic algorithms

Topping, B. H. V.; Sziveri, J.; Bahreinejad, A.; Leite, J. P. B.; Cheng, Bo

doi:10.1016/s0965-9978(97)00062-8

Cited by 41 publications

(11 citation statements)

References 31 publications

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“…All profiles were divided into 10 layers of different thickness. Parallel calculations using the farming algorithm (each processor executes code in isolation from all other processors except for a master or root processor) [17,18] were used in order to decrease the computing time. The total water inflow into the river system is the sum of runoffs from the areas with these soil profiles.…”

Section: Resultsmentioning

confidence: 99%

Modelling of the Effect of Drained Peat Soils to Water Quality Using MACRO and SOILN Models

et al. 2018

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Abstract:Investigations have indicated that in addition to traditional sources, drained peat soils can be considered as a significant source of nitrogen in Estonia. This changes the measures to improve water quality in rivers. At present modelling of nitrogen in rivers has been concentrated on influence of agricultural activity. However, drained peat can increase nitrogen concentrations even without fertilization and farming activities. This investigation describes the attempt to model water quality in the watershed with large share of drained peat soils. A good alignment between measured and modelled nitrate concentrations using the MACRO and the SOILN for MACRO models was shown.

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Section: Resultsmentioning

confidence: 99%

Modelling of the Effect of Drained Peat Soils to Water Quality Using MACRO and SOILN Models

et al. 2018

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“…Jinghui et al (1996), Hajela and Lee (1997), and Papadrakakis et al (1998) use the BP algorithm to improve the efficiency of genetic algorithms for structural optimization problems. Topping et al (1998) present parallel finite element analysis on a MIMD distributed computer. They describe a mesh partitioning technique for planar finite element meshes where a BP neural network is used to find the approximate number of elements within a coarse mesh.…”

Section: Genetic Algorithmsmentioning

confidence: 99%

Neural Networks in Civil Engineering: 1989–2000

Adeli

2001

Computer aided Civil Eng

708

344

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The first journal article on neural network application in civil/structural engineering was published in this journal in 1989. This article reviews neural network articles published in archival research journals since then. The emphasis of the review is on the two fields of structural engineering and construction engineering and management. Neural networks articles published in other civil engineering areas are also reviewed, including environmental and water resources engineering, traffic engineering, highway engineering, and geotechnical engineering. The great majority of civil engineering applications of neural networks are based on the simple backpropagation algorithm. Applications of other recent, more powerful and efficient neural networks models are also reviewed. Recent works on integration of neural networks with other computing paradigms such as genetic algorithm, fuzzy logic, and wavelet to enhance the performance of neural network models are presented.

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“…Further, by dividing a large population size into N p smaller populations, the chance of obtaining the global optimum is decreased. Topping et al (1998) report the ineffectiveness of the reproduction for smaller population sizes, "resulting in incestuous crossbreeding and premature convergence." But, a large population for a large structure consisting of thousands of members requires an inordinate amount of processing time.…”

Section: Data Parallel Fuzzy Genetic Algorithm For Optimization Of Spmentioning

confidence: 99%

“…One of the early applications of GA in structural optimization is presented by Adeli and Cheng (1993). Many other structural engineering applications of GA have been published in recent years, for example, Adeli and Cheng (1994a,b), Topping et al (1998), Jenkins (1998), Hajela et al (1998), Koumousis and Arsenis (1998), Lin and Yang (1998), Rajeev and Krishnamoorthy (1998), Thierauf and Cai (1998), Soh and Yang (1998), Park and Grierson (1999), Savic et al (1999), and Yeh (1999).…”

Section: Introductionmentioning

confidence: 99%

Data Parallel Fuzzy Genetic Algorithm for Cost Optimization of Large Space Steel Structures

Sarma¹,

Adeli

2003

International Journal of Space Structures

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A data parallel fuzzy genetic algorithm is presented for cost optimization of large space steel structures subjected to commonly-used design codes such as the American Institute of Steel Construction (AISC) Allowable Stress Design (ASD) and Load and Resistance Factor Design (LRFD) specifications. The algorithm is implemented on a distributed shared-memory multiprocessor, Origin 2000, using the OpenMP API (Application Programming Interface), and evaluated through application to two large space structures including a 144-storey moment resisting space building structure with more than 20,000 members.

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Parallel processing, neural networks and genetic algorithms

Cited by 41 publications

References 31 publications

Modelling of the Effect of Drained Peat Soils to Water Quality Using MACRO and SOILN Models

Modelling of the Effect of Drained Peat Soils to Water Quality Using MACRO and SOILN Models

Neural Networks in Civil Engineering: 1989–2000

Data Parallel Fuzzy Genetic Algorithm for Cost Optimization of Large Space Steel Structures

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