Optimum design of structures by an improved genetic algorithm using neural networks

Salajegheh, E.; Gholizadeh, Saeed

doi:10.1016/j.advengsoft.2005.03.022

Cited by 93 publications

(33 citation statements)

References 13 publications

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“…As shown in Ref. [7] the computational work by VSP is less than the standard GA. Despite the serious reducing effects of VSP on the optimization time, the computational burden of the process due to implementing the time history dynamic analysis is very high.…”

Section: Optimization Methodsmentioning

confidence: 99%

Dynamic Optimization of Structures Subjected to Earthquake

Lavaei¹,

Lohrasbi²

2016

IJET

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Abstract-To reduce the overall time of structural optimization for earthquake loads two strategies are adopted. In the first strategy, a neural system consisting self-organizing map and radial basis function neural networks, is utilized to predict the time history responses. In this case, the input space is classified by employing a self-organizing map neural network. Then a distinct RBF neural network is trained in each class. In the second strategy, an improved genetic algorithm is employed to find the optimum design. A 72-bar space truss is designed for optimal weight using exact and approximate analysis for the El Centro (S-E 1940) earthquake loading. The numerical results demonstrate the computational advantages and effectiveness of the proposed method.

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Section: Optimization Methodsmentioning

confidence: 99%

Dynamic Optimization of Structures Subjected to Earthquake

Lavaei¹,

Lohrasbi²

2016

IJET

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“…Furthermore, as the BPN is based on the gradient information of the error function, when the problems are complex or the gradient information is hard to obtain, BPN may be helpless. To overcome the disadvantages, many optimization algorithms have been introduced in the study and design of neural networks such as constructing a neural network based on the particle swarm optimization algorithm [47], and using evolutionary algorithms to optimize the neural networks [48][49][50], which have been proved feasible and effective.…”

Section: Discussionmentioning

confidence: 99%

Forecasting of Industrial Water Demand Using Case-Based Reasoning—A Case Study in Zhangye City, China

Yang

Zheng

2017

Water

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Forecasting the industrial water demand accurately is crucial for sustainable water resource management. This study investigates industrial water demand forecasting by case-based reasoning (CBR) in an arid area, with a case study of Zhangye, China. We constructed a case base with 420 original cases of 28 cities in China, extracted six attributes of the industrial water demand, and employed a back propagation neural network (BPN) to weight each attribute, as well as the grey incidence analysis (GIA) to calculate the similarities between target case and original cases.The forecasting values were calculated by weighted similarities. The results show that the industrial water demand of Zhangye in 2030, which is the t arget case, will reach 11.9 million tons. There are 10 original cases which have relatively high similarities to the target case. Furthermore, the case of Yinchuan, 2010, has the largest similarity, followed by Yinchuan, 2009, and Urumqi, 2009. We also made a comparison experiment in which case-based reasoning is more accurate than the grey forecast model and BPN in water demand forecasting. It is expected that the results of this study will provide references to water resources management and planning.

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“…One of the most important characteristics of neural networks is learning. Artificial neural networks have two operation modes, training mode and normal mode [30]. In training mode, adjustable parameters of the networks are modified.…”

Section: Introductionmentioning

confidence: 99%

Process modeling and evaluation of petroleum refinery wastewater treatment through response surface methodology and artificial neural network in a photocatalytic reactor using poly ethyleneimine (PEI)/titania (TiO2) multilayer film on quartz tube

et al. 2014

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In this study, poly ethyleneimine (PEI)/Titania (TiO 2 ) multilayer film on quartz tubes have been successfully fabricated via a layer-by-layer (LbL) self-assembly method. Scanning electron microscopy (SEM) and Brunauer-Emmett-Teller (BET) surface area analysis were carried out for characterization of the layers on quartz tube. The SEM pictures showed that the film surface is smooth and uniform. The BET characterization verified the formation of multilayer thin film. The photocatalytic activity of the PEI/TiO 2 multilayer deposited on the quartz tubes was evaluated in the treatment of raw petroleum refinery wastewater (PRW) under UV light irradiation in three annular photocatalytic reactors. This study examined the impact of initial chemical oxygen demand (COD) concentration, H 2 O 2 concentration, pH and reaction time on the PRW treatment and the results were used to generate both a response surface methodology (RSM) model and an artificial neural network (ANN) model. Maximum COD removal (98 %) was achieved at the optimum conditions (initial COD concentration of 300 mg/l, hydrogen peroxide concentration of 8.8 mM, pH of 5 and reaction time of 120 min). A comparison between the model results and experimental data gave a high correlation coefficient (R ANN 2 = 0.9632, R RSM 2 = 0.943) and showed that two models were able to predict COD removal from PRW by PEI/TiO 2 /UV process. However, ANN model was superior to RSM model with higher value of coefficient of determination (0.9632 ANN [ 0.94 RSM ) and the lower root mean square error (RMSE) (3.377 AAN \ 3.569 RSM ). The average percentage error for ANN and RSM models was 0.18 and 0.73, respectively, indicating the superiority of ANN in capturing the nonlinear behavior of the system. It was clear that the best networks were able to predict the experimental responses more accurately than the multiple regression analysis.

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Optimum design of structures by an improved genetic algorithm using neural networks

Cited by 93 publications

References 13 publications

Dynamic Optimization of Structures Subjected to Earthquake

Dynamic Optimization of Structures Subjected to Earthquake

Forecasting of Industrial Water Demand Using Case-Based Reasoning—A Case Study in Zhangye City, China

Process modeling and evaluation of petroleum refinery wastewater treatment through response surface methodology and artificial neural network in a photocatalytic reactor using poly ethyleneimine (PEI)/titania (TiO2) multilayer film on quartz tube

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