جستجوی مقالات مرتبط با کلیدواژه
تکرار جستجوی کلیدواژه genetic algorithms در نشریات گروه فنی و مهندسی
genetic algorithms
در نشریات گروه مواد و متالورژی
تکرار جستجوی کلیدواژه genetic algorithms در مقالات مجلات علمی
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Limited resources and budget are the most important problem facing the road management sector; therefore, apportionment of maintenance and rehabilitation (M&R) requirements and priorities at the right time and logical are the most significant factors. Roadway will request continuous (M&R) works to avoid deterioration result from repetitive vehicle weight as well as other factors such as environmental factors. Whether, with the allocation budget that was allocated for roadway maintenance work; there is a necessity to efficiently used the obtainable funding. To execute this, a systematic approach for planning M-and-R process to reach optimum the benefits from roadway segment and minimize necessary funding and costs to repeat the pavement into first state. This process defined as the pavement maintenance management system (PMMS); thus, approach would enable agency to allotted funds, labors, equipment and other resources, most efficiently. This paper demonstrates the applying process of the maintenance program according to the genetic algorithm optimization. The aim of it was to obtain the optimal maintenance strategy alternative percent to reach best values for service life extended as well as increasing the pavement condition index (PCI) along with a specific budget that is not sufficient to restore the whole pavement to its previous state. After applying this program, it was found that it gives the road an additional service life (1.2 years), and at the same time it gives an increase in PCI value (3.8%), taking into consideration the limited resources allocated for maintenance.Keywords: Genetic Algorithms, maintenance, optimization, Pavement, roadway
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Intrusion detection systems (IDS) by exploiting Machine learning techniques are able to diagnose attack traffics behaviors. Because of relatively large numbers of features in IDS standard benchmark dataset, like KDD CUP 99 and NSL_KDD, features selection methods play an important role. Optimization algorithms like Genetic algorithms (GA) are capable of finding near-optimum combination of the features intended for construction of the final model. This paper proposes an innovative method called chain method, for evaluation of the given test record. The main intuition of our method is to concentrate merely on one attack type at every stage. In the beginning, datasets with the proposed features by GA based on different labels will be assembled. Based on a specific sequence which is found on different permutation of four existed labels- the test record will be entered the chain module. If the first stage which is correlated to the input sequence-, is able to diagnose the first label, the final output has been indicated. If is not, the records will pass through the next stage until the final output be obtained. Simulations on proposed chain method, illustrate this technique is able to outperform other conventional methods especially in R2L and U2R detection with the accuracy of 98.83% and 98.88% respectively.Keywords: Intrusion Detection Systems, Feature Selection, Genetic Algorithms
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The particle size control of drug is one of the most important factors affecting the efficiency of the nano-drug production in confined liquid impinging jets. In the present research, for this investigation the confined liquid impinging jet was used to produce nanoparticles of Carbamazepine. The effects of several parameters such as concentration, solution and anti-solvent flow rate and solvent type were investigated. So far no analytical and acceptable model has been provided to predict the Carbamazepine particle size in confined liquid impinging jets. In this study the variables affecting the size of the particle became dimensionless using the dimensional analysis then by solving the equation with singular value decomposition method, a simple dimensionless relation was obtained for this process. Moreover, using the genetic algorithm the coefficients of dimensionless parameters were optimally extracted to minimize the error between the model and the laboratory outputs. The determination coefficient of the equation obtained by singular value decomposition method and the improved equation using genetic algorithm were obtained as 0.5291 and 0.5697, respectively. For such a complex experimental system, the accuracy of the obtained equations in spite of their simplicity is acceptable. The obtained results were compared with the results of the neural network model. The results showed that despite the higher precision of the obtained relations by the neural network, the relations obtained by singular value decomposition can be used as a simple method using the dimensionless parameters with acceptable acuracy to predict the particle size in confined liquid impinging jets.Keywords: CLIJ, SVD, Genetic algorithms, nano, drugs
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This paper presents a method concerning the integration of the benefit/cost analysis and the real genetic algorithm with various elements of reservoir dam design. The version 4.0 of HEC-RAS software and Hydro-Rout models have been used to simulate the region and flood routing in the reservoir of the dam, respectively. A mathematical programming has been prepared in MATLAB software and linked with the optimal programming then employed to maximize the benefit/cost ratio of the reservoir dam construction. After a sensitivity analysis, mutation and crossover probability are assumed to be 0.05 and 0.7, respectively. The objective function of the study is benefit/cost ratio. The combined methodology has been provided to help to compute the optimal normal water level, length of spillway and downstream levee height of a reservoir dam considering flood control and cost of construction. This is the first attempt to optimize these important parameters, in the construction of a reservoir dam, together considering flood control and economical aspects. It has been displayed that the proposed method provides strong and suitable solutions to determine these parameters. The results showed that there is potential for application of genetic algorithms to such optimization problems, where the objective function is nonlinear and other optimization techniques may be troublesome to apply and find the global optimum.Keywords: Optimization, Normal Water Level, Spillway Length, Genetic Algorithms, MATLAB Software, Reservoir Dam, Sonateh Dam
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In this paper, the effect of number and fault current limiter (FCL) location has been investigated in order to have maximum reduction of short circuit current level in all buses in a real network. To do so, the faulty buses were identified in terms of short circuit current level by computing short circuits on the desired network. Then, while the fault current limit was modeled, its optimal location and amount for the greatest reduction in the fault current level of the whole critical buses was determined. Optimization computations have been done using the genetic algorithm and method of reducing the search space and all implementation stages of the proposed algorithm and reduction of search space has been conducted in DIgSILENT software using programming language DPL. The results indicate the high efficiency of the proposed method in reducing the short circuit current level of faulty buses and simultaneous improving the power quality.Keywords: Fault current limiter, Short circuit capacity, Genetic algorithms, DIgSILENT
نکته
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