In this article, it has been aimed to predict the shear strength of short circular reinforced-concrete columns using the meta-heuristic algorithms. Based on the studies conducted so far, the parameters dominantly affe...
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In this article, it has been aimed to predict the shear strength of short circular reinforced-concrete columns using the meta-heuristic algorithms. Based on the studies conducted so far, the parameters dominantly affecting the shear strength include axial force, longitudinal and transverse reinforcement, column dimension ratio, concrete compressive strength and ductility. In this respect, first, 200 numerical models of the short circular reinforced-concrete column incorporating various effective parameters so that a sufficient number of outputs could be provided, are analyzed by ABAQUS software to compute their shear strengths. Then, the gene expression programming and particle swarm optimization algorithms are employed to predict the shear strengths and by means of each algorithm, a relation was proposed accordingly. Then, using the experimental data, these relations are evaluated by comparing with those specified in ACI 318 and ASCE-ACI 426. The results indicate that the percentage of relative error between the experimental data and the values obtained from ACI 318 and ASCE-ACI 426 is respectively equal to 25% and 30%, which have been reduced to 13% and 9% through the gene expression programming and particle swarm optimization algorithms implying the satisfactory performance of these two algorithms. Finally, a comparison of the gene expression programming and particleswarmoptimization is investigated in terms of convergence rate, degree of accuracy, and performance mechanism.
The paper proposed a Personalize Parking Guidance Service(PPGS), in which a bi-level programming model was built to describe the relationship between the Personalized Parking Guidance Information System and drivers. T...
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The paper proposed a Personalize Parking Guidance Service(PPGS), in which a bi-level programming model was built to describe the relationship between the Personalized Parking Guidance Information System and drivers. The upper-level of the model aimed to achieve the efficetive and balanced utilization of parking resonurces during peak time, while the lower-level model was to minimize the driver's walking distance after parking.A nested Particel swarmoptimizationalgorithm was used to solve the proposed model. The simulation results of the model show that the peak congestion time has been reduced remarkably under the guidance of the proposed model. The Mean value of Unocuupied Parking Difference Index(MUPDI) curves trend to decline during the overall process. It means that within the acceptable walking distance, the proposed parking lots allocation model can effectively balance the utilization of parking resources shared in the service area and minimize walking distance as well.
The aim of this study is to implement the multi-input-multi-output optimization of reactivity-controlled compression-ignition combustion in a heavy-duty diesel engine running on natural gas and diesel fuel. A single-c...
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The aim of this study is to implement the multi-input-multi-output optimization of reactivity-controlled compression-ignition combustion in a heavy-duty diesel engine running on natural gas and diesel fuel. A single-cylinder heavy-duty diesel engine with a modified bathtub piston bowl profile is set on operation at 9.4 bar indicated mean effective pressure and running at a fixed engine speed of 1300 r/min. A certain amount of diesel fuel mass per cycle is fed into the engine at a fixed equivalence ratio without any exhaust gas recirculation. The optimization targets include reduction in engine emissions as much as possible, avoiding diesel knock occurrence, and achieving low temperature combustion concept with the least or no engine power losses. To implement the optimization, the effects of three control factors on the engine performance are assessed by the design of experiment concept-fractional factorial method. These selected control factors are intake temperature and intake pressure (both at intake valve closing) and the diesel fuel start of injection timing. Some randomized treatment combinations of chosen levels from the three selected control factors are employed to simulate reactivity-controlled compression-ignition combustion. Based on the engine's responses derived from the simulation, reactivity-controlled compression-ignition combustion's mathematical model is identified directly using an artificial neural network. Next, an optimization process is conducted using two different optimizationalgorithms, namely, genetic algorithm and particle swarm optimization algorithm. For assessing and validating the obtained optimal results, the obtained data are used to simulate reactivity-controlled compression-ignition combustion as the engine input factors. The results show that the proposed artificial neural network design is effectively capable of identifying reactivity-controlled compression-ignition combustion's mathematical model. Also, by optimizing reactivi
In the design of quasi-optical systems, the cross-polarization introduced by off-axis mirrors is a major concern to us. By Gaussian beam mode analysis (GBMA), the electric field could be predicted precisely with this ...
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In the design of quasi-optical systems, the cross-polarization introduced by off-axis mirrors is a major concern to us. By Gaussian beam mode analysis (GBMA), the electric field could be predicted precisely with this factor. In this study, we present some significant expressions and a method based on the energy of modes to analyze the influence of design parameters such as the distance between two objects and the incident angles of beams on the cross-polarization level of a two-dimensional (2D) multi-reflector system with a feed containing cross-polarization. Finally, combining these discussions, an innovative 2D quasi-optical system design algorithm with low cross-polarization based on the particleswarmoptimization (PSO) algorithm is proposed considering various system variables. In order to verify the validity and accuracy of our method, we compare the results of our approach and physical optics (PO) in the commercial software package GRASP10 with the example of a double ellipsoidal mirror system.
To make up the poor quality defects of traditional control methods and meet the growing requirements of accuracy for strip crown,an optimized model based on support vector machine(SVM)is put forward firstly to enhance...
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To make up the poor quality defects of traditional control methods and meet the growing requirements of accuracy for strip crown,an optimized model based on support vector machine(SVM)is put forward firstly to enhance the quality of product in hot strip ***,for enriching data information and ensuring data quality,experimental data were collected from a hot-rolled plant to set up prediction models,as well as the prediction performance of models was evaluated by calculating multiple ***,the traditional SVM model and the combined prediction models with particleswarmoptimization(PSO)algorithm and the principal component analysis combined with cuckoo search(PCA-CS)optimization strategies are presented to make a ***,the prediction performance comparisons of the three models are ***,the experimental results revealed that the PCA-CS-SVM model has the highest prediction accuracy and the fastest convergence ***,the root mean squared error(RMSE)of PCA-CS-SVM model is 2.04μm,and 98.15%of prediction data have an absolute error of less than 4.5μ***,the results also proved that PCA-CS-SVM model not only satisfies precision requirement but also has certain guiding significance for the actual production of hot strip rolling.
The increasing demand for solar energy drives the mass production of diverse photovoltaic (PV) systems and, consequently, the growth of used solar panels and their environmental footprint. This study applied a new hyb...
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The increasing demand for solar energy drives the mass production of diverse photovoltaic (PV) systems and, consequently, the growth of used solar panels and their environmental footprint. This study applied a new hybrid optimization method based on particleswarm and ant colony optimizationalgorithms to solve the problems of PV module toxicity. The Weibull distribution function was used to measure the service life of PV modules under a variety of failure scenarios. The simulation results show that PV modules that were guaranteed to have the service life of 25-30 years mostly last 20-25 years. The toxicity coefficient and the use of a hybrid method suggest that the time period when a solar module exhibits a maximum efficiency with a minimal environmental footprint ranges from 15 to 20 years. It was established that this interval corresponds to the level at which the amount of waste does not exceed the amount of energy generated with a minimum number of failures. The proposal will be effective in predicting the performance of solar systems. This approach can be improved in terms of cost and benefit and employed in the future research on renewable energy and ecosystems.
The traditional intelligent neural network PID control method is not conducive to the estimation of the quantity control of the island intelligent tourism. In this paper, a particleswarmoptimization (PSO) algorithm ...
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The traditional intelligent neural network PID control method is not conducive to the estimation of the quantity control of the island intelligent tourism. In this paper, a particleswarmoptimization (PSO) algorithm is proposed to estimate the tourist volume of island intelligent tourism. The fuzzy PID control method is used to establish the intelligent output scheduling control model. In this paper, the adaptive scheduling weighting coefficient of island tourism is introduced, and the radial optimization of iterative steps of particleswarmoptimization is used to estimate the island intelligence tourism. On this basis, a mathematical model of intelligent tourism evaluation based on data mining theory is established. The simulation results show that the algorithm has high precision and good convergence, and improves the robustness of island tourism scheduling control.
Based on cultural algorithm and classical particleswarmoptimization (PSO) algorithm, a cultural particleswarmoptimization (CPSO) algorithm is proposed. In the improved algorithm, double evolutionary mechanisms are...
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ISBN:
(纸本)9781457720727
Based on cultural algorithm and classical particleswarmoptimization (PSO) algorithm, a cultural particleswarmoptimization (CPSO) algorithm is proposed. In the improved algorithm, double evolutionary mechanisms are used. The population space and the belief space of cultural algorithm are redesigned. The proposed model was used to solve the partner selection problem of virtual enterprise. In a virtual enterprise, the whole task can be accomplished by the cooperation among those candidate partners. The optimal objective is to minimize the total cost and completing time. Finally, the performance of the algorithm is evaluated by simulations. Results demonstrate the feasibility and efficiency of the proposed algorithm.
The wear of the piston ring-cylinder system is inevitable in the operation of the internal combustion engines (ICEs). If wear exceeds the maximum, the piston ring-cylinder system will be failure. A novel wear assessme...
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The wear of the piston ring-cylinder system is inevitable in the operation of the internal combustion engines (ICEs). If wear exceeds the maximum, the piston ring-cylinder system will be failure. A novel wear assessment model is proposed based on the support vector regression, and the fuzzy uncertainty is modeled to describe the random behavior under small sample. To verify the proposed model, the sample data of cylinder liner wear is applied. For best results, the particleswarmoptimization (PSO) algorithm is used to optimize the model parameters. A back propagation neural network (BPNN) is employed to verify the effectiveness of the proposed model. The results show that the novel support vector regression has better prediction accuracy than other methods for cylinder wear in this paper, the proposed model can evaluate the cylinder liner wear of the ICEs effectively. The work provides a technical support for evaluating the service performance of the piston ring-cylinder liner and a reference for regular maintenance of the ships.
With the development of intelligent applications of the Internet of things,the sharp increase of data scale and the improvement of computational model complexity in the edge computing environment put forward higher re...
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With the development of intelligent applications of the Internet of things,the sharp increase of data scale and the improvement of computational model complexity in the edge computing environment put forward higher requirements for the performance of edge *** optimizing the scheduling of AI data intensive computing tasks can greatly improve the overall performance of edge ***,a novel particle swarm optimization algorithm is proposed to realize task scheduling in edge computing environment by calculating task scheduling to optimize task execution time and scheduling *** the speed formula of particle swarm optimization algorithm to improve the convergence speed and row performance of the ***,on the Cloudsim simulation platform,the proposed particle group algorithm LK-PSO was compared with the other four benchmark *** results showed that the method task scheduling cost and execution time were effectively *** method can effectively improve the resource utilization rate of marginal computing and improve the efficiency of marginal computing power.
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