The design of engineering systems often involves multiple disciplines and competing objectives, which requires coordination, information exchange and share amongst the disciplines. However, in practical design environ...
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The design of engineering systems often involves multiple disciplines and competing objectives, which requires coordination, information exchange and share amongst the disciplines. However, in practical design environments, designers have to make decisions in isolation due to organization barriers, time schedules and geographical constraints. This paper will propose a new approach for the multi-objective multidisciplinary design optimization (MDO) problems in non-cooperative environments based on gene expression programming (GEP) and Nash equilibrium in the game theory. In this approach, the GEP method is used as a surrogate to construct the approximate rational reaction sets (RRSs) in the Nash model. The effectiveness of the proposed method is demonstrated by the design of a thin-walled pressure vessel and the hull form parameter design of a small waterplane area twin hull (SWATH) ship. The results show that this approach can fully explore and provide the explicit functional relationship between the strategy of an isolated player and the control variables of the other players, thus able to obtain a better Nash equilibrium solution. (C) 2014 Elsevier Ltd. All rights reserved.
Analyses and applications of big data require special technologies to efficiently process large number of data. Mining association rules focus on obtaining relations between data. When mining association rules in big ...
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Analyses and applications of big data require special technologies to efficiently process large number of data. Mining association rules focus on obtaining relations between data. When mining association rules in big data, conventional methods encounter severe problems incurred by the tremendous cost of computing and inefficiency to achieve the goal. This study proposes an evolutionary algorithm to address these problems, namely Niche-Aided gene expression programming (NGEP). The NGEP algorithm (1) divides individuals to several niches to evolve separately and fuses selected niches according to the similarities of the best individuals to ensure the dispersibility of chromosomes, and (2) adjusts the fitness function to adapt to the needs of the underlying applications. A number of experiments have been performed to compare NGEP with the FP-Growth and Apriori algorithms to evaluate the NGEP's performance in mining association rules with a dataset of measurement for environment pressure (Iris dataset) and an Artificial Simulation Database (ASD). Experimental results indicate that NGEP can efficiently achieve more association rules (36 vs. 33 vs. 25 in Iris dataset experiments and 57 vs. 44 vs. 44 in ASD experiments) with a higher accuracy rate (74.8 vs. 53.2 vs. 50.6% in Iris dataset experiments and 95.8 vs. 77.4 vs. 80.3% in ASD experiments) and the time of computing is also much less than the other two methods.
The honey-bees mating programming (HBMP) algorithm is introduced as a novel tool for predicting suspended sediment concentration for the Mad River catchment near Arcata, USA. The paper also applies geneexpression pro...
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The honey-bees mating programming (HBMP) algorithm is introduced as a novel tool for predicting suspended sediment concentration for the Mad River catchment near Arcata, USA. The paper also applies gene expression programming (GEP) as a comparison and shows that these two approaches can the produce transparent, nonlinear relationships between the independent and dependent variables. Some modifications have been made to the HBMP algorithm to improve its capability and efficiency. The results achieved from this method and GEP are compared with two different sediment rating curves based on regression techniques. The findings show that the results from both the HBMP and GEP methods are promising and outperform the results obtained from the sediment rating curves.
The loss of surfactant can reduce the technical and economic efficiency of chemical surfactant flooding in the recovery of residual oil. Retention of surfactant molecules is thus recognized as a fundamental problem in...
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The loss of surfactant can reduce the technical and economic efficiency of chemical surfactant flooding in the recovery of residual oil. Retention of surfactant molecules is thus recognized as a fundamental problem in chemical recovery based methods. Therefore, reliable methods which are able to rapidly estimate the surfactant retention are of importance. In this communication, a new model is proposed for the determination of surfactant retention in porous media during chemical flooding. The mathematical algorithm adopted in the development of the model is gene expression programming (GEP). The input parameters for the new model are temperature, maximum effluent pH, reservoir rock type i.e., carbonated or sandstone, co-solvent concentration, average molecular weight of surfactant mixture, total acid number (TAN), absolute permeability, mobility ratio, and salinity of polymer. Several statistical and graphical error analyses were applied to assess the performance and accuracy of the proposed model. A comparison was also performed between the newly developed model, a smart method, and a previously published empirical correlation available in literature. The newly developed model performs, overall, superior to the methods compared. Estimations were found to be within acceptable agreement with the literature-reported data of surfactant retention, with an average absolute relative deviation of approximately 16.6%, and a R-squared value of 0.92. (C) 2015 Elsevier Ltd. All rights reserved.
Accurate determination of pressure-volume-temperature (PVT) properties of petroleum reservoirs is essential in material balance calculations, inflow performance, well-test analysis, reservoir simulation, etc. Ideally,...
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Accurate determination of pressure-volume-temperature (PVT) properties of petroleum reservoirs is essential in material balance calculations, inflow performance, well-test analysis, reservoir simulation, etc. Ideally, those data should be obtained experimentally;however, experimental measurements require accurate and enough sampling, and are time consuming, expensive and tedious. Therefore, seeking for a simple, reliable and accurate model for prediction of PVT properties of petroleum systems is of a vital importance. In this communication, a large PVT data bank, covering a wide range of thermodynamic conditions was collected from variety of geographical locations around the world. Afterward, gene expression programming (GEP) was employed to develop a universal model for solution gas: oil ratio. The proposed model is a function of bubble point pressure, gas specific gravity, and oil API gravity, and has a very simple format with only one tuning parameter. The proposed model was compared to both explicit and implicit models available in literature for prediction of solution gas: oil ratio, using statistical and graphical error analyses. The results of this study indicate that the proposed model is more accurate, reliable and efficient compared to all other published correlations. (C) 2015 Elsevier B.V. All rights reserved.
The paper is devoted to diagnostic method enabling us to perform all the three levels of fault investigations - detection, localization and identification. It is designed for analog diode-transistor circuits, in which...
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The paper is devoted to diagnostic method enabling us to perform all the three levels of fault investigations - detection, localization and identification. It is designed for analog diode-transistor circuits, in which the circuit's state is defined by the DC sources' values causing elements operating points and the harmonic components with small amplitudes being calculated in accordance with small-signal circuit analysis rules. gene expression programming (GEP), differential evolution (DE) and genetic algorithms (GA) are a mathematical background of the proposed algorithms. Time consumed by diagnostic process rises rapidly with the increasing number of possible faulty circuit elements in case of using any of mentioned algorithms. The conncept of using two different circuit models with partly different elements allows us to decrease a number of possibly faulty elements in each circuit because some of possibly faulty elements are absent in one of two investigated circuits.
An evolutionary algorithm becomes trapped in local optima when a premature convergence occurs. Research has suggested maintaining population diversity to address this problem. However, traditional methods are excessiv...
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An evolutionary algorithm becomes trapped in local optima when a premature convergence occurs. Research has suggested maintaining population diversity to address this problem. However, traditional methods are excessively complex and time consuming. This study proposes a hybrid selection mechanism in which clonal and roulette wheel selections are alternated to maintain population diversity during evolution. The proposed method is based on a genetic programming technique known as gene expression programming (GEP). The prediction power and efficiency of the proposed method were compared with those of other GEP-based algorithms by using five time series benchmarks. The experimental results indicated that the proposed algorithm outperforms the other algorithms.
gene expression programming was employed to express the relationship between the inputs and the outputs of a single cylinder four-stroke CRDI engine coupled with EGR. The performance and emission parameters (BSFC, BTE...
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gene expression programming was employed to express the relationship between the inputs and the outputs of a single cylinder four-stroke CRDI engine coupled with EGR. The performance and emission parameters (BSFC, BTE, CO2, NOx and PM) have been modelled by gene expression programming where load, fuel injection pressure, EGR and fuel injected per cycle were chosen as input parameters. From the results it was found that the GEP can consistently emulate actual engine performance and emission characteristics proficiently even under different modes of CRDI operation with EGR with significant accuracy. Moreover, the GEP obtained results were also compared with an ANN model, developed on the same parametric ranges. The comparison of the obtained results showed that the GEP model outperforms the ANN model in predicting the desired response variables. (C) 2014 Elsevier Ltd. All rights reserved.
A model was developed for the prediction of the entrainment rate of non-uniform sediment considering the movement of bedforms. Laboratory experiments were conducted to advance the formulations of the proposed model an...
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A model was developed for the prediction of the entrainment rate of non-uniform sediment considering the movement of bedforms. Laboratory experiments were conducted to advance the formulations of the proposed model and to validate and estimate the model parameters. The model parameters were related to the hydraulic conditions of the flow and the properties of the sediment mixtures using dimensional analysis and gene expression programming. The model incorporated four parameters on its formulation, namely the Shields stress and critical Shields stress to describe the hydraulic and sediment conditions of the flow, the Kramer coefficient of uniformity to describe the grain size distribution of a particular sediment mixture, and the relative position of a particular grain size fraction to the geometric mean to describe the entrainment rate of that fraction within the sediment mixture. The proposed model provided satisfactorily predictions with a deviation less than 25% between the measured and predicted values for most of the fractions, which confirms the validity of the proposed approach and model in predicting of the entrainment rates of various fractions. The model predictions were also compared with other models available for the prediction of the entrainment rate of non-uniform sediment. The model predictions were within the same order of magnitude of the other models' predictions. Copyright (c) 2015 John Wiley & Sons, Ltd.
Most modern products/processes usually have several quality characteristics that must be optimized simultaneously;this is called a multi-response parameter design problem. To overcome shortcomings in the literature, i...
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Most modern products/processes usually have several quality characteristics that must be optimized simultaneously;this is called a multi-response parameter design problem. To overcome shortcomings in the literature, including insufficient accuracy of second-order polynomials, subjective determination of relative weights and shape coefficients, and non-consideration of manufacturing or material costs, this paper proposes a cost-based procedure for resolving multi-response parameter design problems using gene expression programming (GEP), Taguchi quality loss, and particle swarm optimization (PSO). A case study with the aim of optimizing the design of a heat sink applied to a high-power MR16 LED lamp was used to demonstrate the proposed procedure. The experimental results indicated that the proposed procedure can provide highly robust settings for design parameters that can maximize the thermal performance and minimize the actual material cost of a heat sink. Furthermore, decisionmakers no longer need to subjectively determine the relative weight of each response. Therefore, the proposed approach can be considered to be feasible and effective;it has the potential to be a useful tool for resolving general multi-response parameter design problems in the real world.
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