With the rapid development of internet technology, the amount of collected or generated data has increased exponentially. The sheer volume, complexity, and unbalanced nature of this data pose a challenge to the scient...
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With the rapid development of internet technology, the amount of collected or generated data has increased exponentially. The sheer volume, complexity, and unbalanced nature of this data pose a challenge to the scientific community to extract meaningful information from this data within a reasonable time. In this paper, we implemented a scalable design of an artificial bee colony for big data classification using Apache Spark. In addition, a new fitness function is proposed to handle unbalanced data. Two experiments were performed using the real unbalanced datasets to assess the performance and scalability of our proposed algorithm. The performance results reveal that our proposed fitness function can efficiently deal with unbalanced datasets and statistically outperforms the existing fitness function in terms of G-mean and F-1-Score. In additon, the scalability results demonstrate that our proposed Spark-based design obtained outstanding speedup and scaleup results that are very close to optimal. In addition, our Spark-based design scales efficiently with increasing data size.
In this research work, Friction Theory and Free Volume Theory are applied to live oil characterized based on SARA TEST for viscosity modeling and make a new model in combination with two equation of state (PR and PCSA...
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In this research work, Friction Theory and Free Volume Theory are applied to live oil characterized based on SARA TEST for viscosity modeling and make a new model in combination with two equation of state (PR and PCSAFT). Parameters for pseudo-components are obtained by tuning the viscosity at atmospheric pressure and temperatures of 10, 20, and 40 ?. A new fitting approach is suggested where the number of fitting parameters is 17 and 12 for FT and FVT model, respectively. These parameters are tuned using the Genetic algorithm and Particle Swarm optimization and make eight new models. The results show that PC-SAFT provides viscosity predictions for all models with less deviation from experimental viscosity. The FT and FVT models have less error for oils with API > 40 and API < 40, respectively. The PC-SAFT + PSO improves the accuracy in viscosity modeling for both FT and FVT models. PSO can play a significant role even more than PC-SAFT.
This paper presents a simple, efficient, real number encoding genetic algorithm. The algorithm has omitted the workload of encoding and selection, it adopts deterministic induced crossover and mutation operators to im...
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ISBN:
(纸本)9780769547923
This paper presents a simple, efficient, real number encoding genetic algorithm. The algorithm has omitted the workload of encoding and selection, it adopts deterministic induced crossover and mutation operators to improve the algorithm's ability of local convergence;And introduced foreign populations by the theory of the bee evolution genetic algorithm, which has strengthened the capacity of mining the information contained in the population optimal individual. This algorithm is not need to improve the overall fitness of the population, but using genetic algorithm processes to achieve the optimal search. We have verified the algorithm through JAVA and MATLAB, the results show that this algorithm can obtain the optimal solution within certain accuracy in 10 generations.
This paper presents a differential evolution optimized fuzzy clustering algorithm (DEOFCA), which combines differential evolution (DE) algorithm and fuzzy clustering theory. Since DE algorithm has strong global search...
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This paper presents a differential evolution optimized fuzzy clustering algorithm (DEOFCA), which combines differential evolution (DE) algorithm and fuzzy clustering theory. Since DE algorithm has strong global search ability and good robustness, DEOFCA uses DE to replace the iteration process of fuzzy C means clustering algorithm, by which the global optimization capability is greatly improved. An adaptive adjusting strategy for control parameters is integrated with the algorithm to eliminate negative effects of the control parameters setting to algorithm performance and efficiency. The proposed algorithm is applied to a case of power system, and the results demonstrate the feasibility and efficiency of this novel method.
In a fuzzy cognitive map-based forecasting model, causal relationships (represented with a weight matrix) are constant. This may hinder the applicability of such a model. In this paper, we propose an adaptive fuzzy co...
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ISBN:
(数字)9781728123486
ISBN:
(纸本)9781728123493
In a fuzzy cognitive map-based forecasting model, causal relationships (represented with a weight matrix) are constant. This may hinder the applicability of such a model. In this paper, we propose an adaptive fuzzy cognitive map-based forecasting model. Different from the existing models, the proposed model is made of a collection of fuzzy cognitive maps. Maps are constructed according to the clustering results of the so-called premises covering an entire time series. Subsequently, we use an optimization algorithm to train parameters of each fuzzy cognitive map individually. The proposed model construction procedure allows forming fuzzy cognitive maps that more flexible and, thus, suitable for forecasting of long time series. In experimental studies on synthetic time series and real time series, the proposed model performed very well in comparison with the original fuzzy cognitive map-based forecasting model and another two forecasting models.
Epsilon Airlines faced the allocation of wheelchair problem. To minimize the cost of providing wheelchair assistance to its passengers, we analysis the trade-off between explicit costs(chairs and personnel) and implic...
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Epsilon Airlines faced the allocation of wheelchair problem. To minimize the cost of providing wheelchair assistance to its passengers, we analysis the trade-off between explicit costs(chairs and personnel) and implicit costs(losses in market share). Then, we develop MultiConcourse Airport Model to simulate the interactions between escorts, wheelchairs, and passengers. In addition,the Airline Competition Model uses game-theoretic in seeking the maximum profits and configuration scheme based on the least cost airlines. To put these models into reality, we incorporate extensive demographic date and run a case study on 2005 Southwest Airlines flight data from Midland TX, Columbus OH, St. Louis MO.
Reservoir history matching refers to the process of continuously adjusting the parameters of the reservoir model, so that its dynamic response will match the historical observation data, which is a prerequisite for ma...
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Reservoir history matching refers to the process of continuously adjusting the parameters of the reservoir model, so that its dynamic response will match the historical observation data, which is a prerequisite for making forecasts based on the reservoir model. With the development of optimization theory and machine learning algorithms, automatic history matching has made numerous breakthroughs for practical applications. In this perspective, the existing automatic history matching methods are summarized and divided into model-driven and surrogate-driven history matching methods according to whether the reservoir simulator needs to be run during the automatic history matching process. Then, the basic principles of these methods and their limitations in practical applications are outlined. Finally, the future trends of reservoir automatic history matching are discussed.
The aim of this paper is to model and simulate a cantilever beam as energy harvester to expose to wind vibrations. A mathematical model describes the behavior of cantilever beam and the electromechanical coupling, usi...
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ISBN:
(纸本)9781479999835
The aim of this paper is to model and simulate a cantilever beam as energy harvester to expose to wind vibrations. A mathematical model describes the behavior of cantilever beam and the electromechanical coupling, using piezoelectric constitutive equations. An experimental setup of a fixed configuration (dimensions, materials, boundaries and shape) is performed by means of such device and the effects caused by the wind force on the cantilever are analyzed. The same device is used for a simulation, implemented with Comsol Multiphysics, in which wind force is simulated like a pressure acting on the cantilever. The comparison between simulation and experimental results validates the simulation method and allows an appropriate choice of the most suitable shape for this kind of cantilever: the choice is carried out using the optimization platform KIMEME.
Transportation facility or automotive service enterprise location is an interesting and important *** improve transportation efficient,lots of researchers have addressed traditional facility location allocation(FLA) p...
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ISBN:
(纸本)9781510823808
Transportation facility or automotive service enterprise location is an interesting and important *** improve transportation efficient,lots of researchers have addressed traditional facility location allocation(FLA) problem,e.g.,the FLA problem with the minimum transportation cost or the maximum obtained *** work aims to optimize obsolete vehicle location with the minimum transportation ***,the return center location on obsolete vehicle is influenced by regional ***,the regional constraint should be considered as factors influencing the FLA *** handle this issue via a more practical method,this work proposes an economic optimal model with region constraints for an automotive service *** is,by taking the vehicle return center as a typical automotive service enterprise and an example,this work presents its new economic optimal models considering region and non-region *** artificial fish swarm algorithm is proposed to solve the proposed *** numerical examples are given to illustrate the proposed models and testify the effectiveness of the algorithm.
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