The paper deals with efficiency comparison of two global evolutionary optimization methods implemented in MATLAB. Attention is turned to an elitist Non-dominated Sorting geneticalgorithm (NSGA-II) and a novel multi-o...
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The paper deals with efficiency comparison of two global evolutionary optimization methods implemented in MATLAB. Attention is turned to an elitist Non-dominated Sorting geneticalgorithm (NSGA-II) and a novel multi-objective Particle Swarm Optimization (PSO). The performance of optimizers is compared on three different test functions and on a cavity resonator synthesis. The microwave resonator is modeled using the Finite Element Method (FEM). The hit rate and the quality of the Pareto front distribution are classified.
This study describes a computationally efficient model for the optimal sizing and siting of Electrical Energy Storage Devices (EESDs) in Smart Grids (SG), accounting for the presence of time-varying electricity tariff...
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This study describes a computationally efficient model for the optimal sizing and siting of Electrical Energy Storage Devices (EESDs) in Smart Grids (SG), accounting for the presence of time-varying electricity tariffs due to Demand Response Program (DRP) participation. The joint planning and operation problem for optimal siting and sizing of the EESD is proposed in a two-stage optimization problem. In this regard, the long-term decision variables deal were the size and location of the EESDs and have been considered at the master level while the operating point of the generation units and EESDs is determined by the slave stage of the model utilizing a standard mixed-integer linear programming model. To examine the effectiveness of the model in the slave sub- problem, the operation model is solved for different working days of different seasons. binary Particle Swarm Optimization (BPSO) and binary genetic algorithm (BGA) have been used at the master level to propose different scenarios for investment in the planning stage. The slave problem optimizes the model in terms of the short-term horizon (day-ahead). Additionally, the slave problem determines the optimal schedule for an SG considering the presence of EESD (with sizes and locations provided by the upper level). The electricity price fluctuates throughout the day, according to a Time-of-Use (ToU) DRP pricing scheme. Moreover, the impacts of DRPs have been addressed in the slave stage. The proposed model is examined on a modified IEEE 24-Bus test system
Fitting wind speed and wind direction probability distribution is a crucial and fundamental task for the investigation of wind resource. This paper proposes a mixture basis(B)-spline function to fit wind speed and win...
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Fitting wind speed and wind direction probability distribution is a crucial and fundamental task for the investigation of wind resource. This paper proposes a mixture basis(B)-spline function to fit wind speed and wind direction probability distribution. Such mixture B-spline function requires no prior assumption and eliminates the influence of subjective experience. A procedure has been developed that combines a binary genetic algorithm, maximum likelihood estimation, and a chi-squared test. This procedure can enable mixture B-spline function to adapt to various probabilistic behaviors. The wind speed and wind direction probability distributions of four North Dakota, U.S. sites are fitted by the mixture B-spline function and other five methods. Their goodness-of-fits are examined by chi-squared tests. The results indicate that the mixture B-spline function passes all the chi-squared tests and presents the highest goodness-of-fit. Additionally, the mixture B-spline function is used to calculate the mean wind power density, which can help identify sites with richer wind resources.
In the traditional methods, optimization and mesh generation in computational fluid dynamics are separate procedures, which increases the time and calculation effort. This issue has been addressed by a shape optimizat...
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In the traditional methods, optimization and mesh generation in computational fluid dynamics are separate procedures, which increases the time and calculation effort. This issue has been addressed by a shape optimization approach based on the binary genetic algorithm coupled with the image-processing method for drag reduction purposes. A system-specified code in MATLAB software is developed to find the optimized configuration. Accurate filters in the image-processing method are used to detect the edge and corners of the imported obstacles in the flow field to generate suitable grids. A turbulent flow field over obstacles was modeled by Chien's low-Reynolds k - epsilon model and finite volume methods with stagger grids to solve governing equations. Combining the binary genetic algorithm with the image-processing method is an effective method to optimize shape configuration and mesh generation. The drag coefficients of the generated shapes are determined and the optimum one is obtained. A good agreement is obtained by comparing the obtained results with wind tunnel experimental data.
In response to the growing demand for fast-charging electric vehicles (EVs), this study presents a novel hybrid multimodule DC-DC converter based on the dual-active bridge (DAB) topology. The converter comprises eight...
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In response to the growing demand for fast-charging electric vehicles (EVs), this study presents a novel hybrid multimodule DC-DC converter based on the dual-active bridge (DAB) topology. The converter comprises eight modules divided into two groups: four Insulated-Gate Bipolar Transistor (IGBT) modules and four Metal-Semiconductor Field-Effect Transistor (MESFET) modules. The former handles high power with a low switching frequency, while the latter caters to lower power with a high switching frequency. This configuration leverages the strengths of both types of semiconductors, enhancing the converter's power efficiency and density. To investigate the converter's performance, a small-signal model is developed, alongside a control strategy to ensure uniform power sharing among the modules. The model is evaluated through simulation using MATLAB, which confirms the uniformity of the charging current provided to EV batteries. The results show an impressive power efficiency of 99.25% and a power density of 10.99 kW/L, achieved through the utilization of fast-switching MESFETs and the DAB topology. This research suggests that the hybrid multimodule DC-DC converter is a promising solution for fast-charging EVs, providing high efficiency, power density, and switching speed. Future studies could explore the incorporation of advanced wide bandgap devices to handle even larger power fractions.
Recently, many companies move to use cloud computing systems to enhance their performance and productivity. Using these cloud computing systems allows the execution of applications, data, and infrastructures on cloud ...
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Recently, many companies move to use cloud computing systems to enhance their performance and productivity. Using these cloud computing systems allows the execution of applications, data, and infrastructures on cloud platforms (i.e., online), which increase the number of attacks on such systems. As a resulting, building robust Intrusion detection systems (IDS) is needed. The main goal of IDS is to detect normal and abnormal network traffic. In this paper, we propose a hybrid approach between an Enhanced binary genetic algorithms (EBGA) as a wrapper feature selection (FS) algorithm and Long Short-Term Memory (LSTM). A novel injection method to prevent premature convergence of the GA is proposed in this paper. An intelligent k-means algorithm is employed to examine the solution distribution in the search space. Once 80% of the solutions belong to one cluster, an injection method (i.e., add new solutions) is used to redistribute the solutions over the search space. EBGA will reduce the search space as a preprocessing step, while LSTM works as a binary classification method. UNSW-NB15, a real-world public dataset, is used in this work to evaluate the proposed system. The obtained results show the ability of feature selection method to enhance the overall performance of LSTM.
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