scholarly journals Hybrid solar-gas-electric dryer optimization with genetic algorithms

2018 ◽  
Author(s):  
Naji Abdenouri ◽  
H. El Ferouali ◽  
M. Gharafi ◽  
A. Zoukit ◽  
S. Doubabi
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Response Time ◽  
Cfd Simulation ◽  
Learning Algorithm ◽  
Fitness Function ◽  
Solar Absorber ◽  
Chamber Temperature ◽  
Artificial Neural ◽  
Drying Chamber

To promote the hybrid solar dryers for use even under unfavorable weather and to overcome the intermittance state issue, the energy consumption should be optimized and the response time should be reduced. This work concerns a drying chamber connected to a solar absorber where the air can be heated also by combustion of gas and by electric resistance. To optimize the control parameters, an evolutionary optimization algorithm simulating natural selection was used. It was combined with a predictive model based on the artificial neural networks (ANN) technique and used as a fitness function for the genetic algorithm (GA). The ANN is a learning algorithm that needs training through a large dataset, which was collected using CFD simulation and experimental data. Then a GA was executed in order to optimize two objectives: The energy consumption and the t95% response time in which the drying chamber temperature reaches its set point (60°C). After optimization, a 30% decrease of the t95% response time, and 20% decrease of the energy consumption were obtained.   Keywords: hybrid solar dryer; artificial neural network; temperature regulation; energy consumption; genetic algorithm. 

Energy-Balanced Routing Method for Wireless Cooperative Wireless Network

2013 ◽  
Vol 774-776 ◽  
pp. 1659-1663
Author(s):  
Yan Xin Yao ◽  
Qiu Shi Liu
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Wireless Network ◽  
Fitness Function ◽  
Path Loss ◽  
New Method ◽  
Transmission Path ◽  
Routing Method ◽  
Energy Balanced Routing

This paper presents a new method for optimizing energy consumption of wireless network. This new method tries to keep the energy consumption of the whole network while balancing the energy consumption of each node. In particular, we focus on the routing method to shorten the transmission path for reducing the energy path loss. We perform this by introducing an appropriate fitness function with the Genetic Algorithm. This fitness function is designed in a dedicate way so that the energy consumption minimization and energy consumption balance between nodes could be fulfilled simultaneously. Simulations validate that the proposed method could keep energy consumption and balance the energy consumption simultaneously to a better extent.

scholarly journals Mitigating Economic Denial of Sustainability (EDoS) in Cloud Environment using Genetic Algorithm and Artificial Neural Network

2019 ◽  
Vol 8 (10) ◽  
pp. 3415-3421
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Virtual Machines ◽  
Fitness Function ◽  
Medium Size ◽  
Cloud Environment ◽  
Edos Attacks ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Economic Denial of Sustainability (EDoS) is a latest threat in the cloud environment in which EDoS attackers continually request huge number of resources that includes virtual machines, virtual security devices, virtual networking devices, databases and so on to slowly exploit illegal traffic to trigger cloud-based scaling capabilities. As a result, the targeted cloud ends with a consumer bill that could lead to bankruptcy. This paper proposes an intelligent reactive approach that utilizes Genetic Algorithm and Artificial Neural Network (GANN) for classification of cloud server consumer to minimize the effect of EDoS attacks and will be beneficial to small and medium size organizations. EDoS attack encounters the illegal traffic so the work is progressed into two phases: Artificial Neural Network (ANN) is used to determine affected path and to detect suspected service provider out of the detected affected route which further consist of training and testing phase. The properties of every server are optimized by using an appropriate fitness function of Genetic Algorithm (GA) based on energy consumption of server. ANN considered these properties to train the system to distinguish between the genuine overwhelmed server and EDoS attack affected server. The experimental results show that the proposed Genetic and Artificial Neural Network (GANN) algorithm performs better compared to existing Fuzzy Entropy and Lion Neural Learner (FLNL) technique with values of precision, recall and f-measure are increased by 3.37%, 10.26% and 6.93% respectively.

Optimization of Clustering in Wireless Sensor Networks Using Genetic Algorithm

2020 ◽  
pp. 822-836
Author(s):  
Pritee Parwekar ◽  
Sireesha Rodda
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Network ◽  
Sensor Node ◽  
Fitness Function ◽  
Wireless Sensor ◽  
Sleep Mode ◽  
Communication Distance ◽  
Selection Of

The energy of a sensor node is a major factor for life of a network in wireless sensor network. The depletion of the sensor energy is dependent on the communication range from the sink. Clustering is mainly used to prolong the life of a network with energy consumption. This paper proposes optimization of clustering using genetic algorithm which will help to minimize the communication distance. The cluster overhead and the active and sleep mode of a sensor is also considered while calculating the fitness function to form the cluster. This approach helps to prolong the network life of sensor network. The proposed work is tested for different number of nodes and is helping to find the correct solution for the selection of cluster heads.

Optimization of Clustering in Wireless Sensor Networks Using Genetic Algorithm

2017 ◽  
Vol 8 (4) ◽  
pp. 84-98 ◽  
Author(s):  
Pritee Parwekar ◽  
Sireesha Rodda
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Network ◽  
Sensor Node ◽  
Fitness Function ◽  
Wireless Sensor ◽  
Sleep Mode ◽  
Communication Distance ◽  
Selection Of

The energy of a sensor node is a major factor for life of a network in wireless sensor network. The depletion of the sensor energy is dependent on the communication range from the sink. Clustering is mainly used to prolong the life of a network with energy consumption. This paper proposes optimization of clustering using genetic algorithm which will help to minimize the communication distance. The cluster overhead and the active and sleep mode of a sensor is also considered while calculating the fitness function to form the cluster. This approach helps to prolong the network life of sensor network. The proposed work is tested for different number of nodes and is helping to find the correct solution for the selection of cluster heads.

scholarly journals Analyzing student performance using evolutionary artificial neural network algorithm

2018 ◽  
Vol 7 (2.26) ◽  
pp. 67 ◽  
Author(s):  
A S. Arunachalam ◽  
T Velmurugan
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Student Performance ◽  
Fitness Function ◽  
Research Work ◽  
Function Evaluation ◽  
Success Factor ◽  
Optimization Approach ◽  
Artificial Neural

Educational Data Mining (EDM) and Learning Systematic (LS) research have appeared as motivating areas of research, which are clarifying beneficial understanding from educational databases for many purposes such as predicting student’s success factor. The ability to predict a student’s performance can be beneficial in modern educational systems. This research work aims at developing an evolutionary approach based on genetic algorithm and the artificial neural network. The traditional artificial neural network lacks predicting student performance due to the poor modeling structure and the capability of assigning proper weights to each node under the hidden layer. This problem is overwhelmed with the aid of genetic algorithm optimization approach which produces appropriate fitness function evaluation in each iteration of the learning process. The performances gradually increase the accuracy of the prediction and classification more precisely.

Experimental, modeling and optimization study on the mechanical properties of epoxy/high-impact polystyrene/multi-walled carbon nanotube ternary nanocomposite using artificial neural network and genetic algorithm

2013 ◽  
Vol 20 (3) ◽  
pp. 265-276 ◽  
Author(s):  
Abdolhosein Fereidoon ◽  
Amin Hamed Mashhadzadeh ◽  
Yasser Rostamiyan
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Fitness Function ◽  
Optimization Method ◽  
High Impact ◽  
Effective Parameters ◽  
High Impact Polystyrene ◽  
Artificial Neural

AbstractIn spite of Epoxy resin’s good tensile strength, are brittle in nature and have poor resistance at the front of crack propagation. In enhancing simultaneously the mechanical strength and fracture toughness of epoxy-based nanocomposites, high-impact polystyrene (HIPS) as thermoplastic phase and multi-walled carbon nanotubes (MWCNT) as nanofiller phases are used incorporately to obtain ternary epoxy-based nanocomposites. Tensile, flexural, compression and impact are the four different mechanical properties. Artificial neural network was used to present models for predicting the mechanical behavior of epoxy/HIPS/MWCNT nanocomposites. Also, this model used as a fitness function of genetic algorithm as a powerful optimization method to find the optimum value of the above-mentioned mechanical properties. The effective parameters investigated were HIPS, MWCNT and hardener. From the result, it was found that the combination of HIPS and MWCNT nanofillers significantly increases tensile, compression and impact strength of neat resin by up to 52%, 43% and 334%, respectively, but flexural strength did not change positively. Also, elongation at break for tensile, flexural and compression rose to 223%, 36% and 26% of neat epoxy, respectively. The morphology of fracture surface was studied by scanning electron microscopy.

Genetic Optimization of Energy Consumption of Pellet Shaft Furnace Combustor Based on Support Vector Machine (SVM)

2014 ◽  
Vol 12 (1) ◽  
pp. 205-214 ◽  
Author(s):  
Xi Chen ◽  
Wenqi Zhong ◽  
Tiancai Wang ◽  
Fei Liu ◽  
Zhi Zhang
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Energy Consumption ◽  
Shaft Furnace ◽  
Fitness Function ◽  
Optimization Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Genetic Optimization ◽  
Gas Consumption

Abstract Investigation on optimization of pellet shaft furnace based on the combination of genetic algorithm and support vector machine (SVM) is carried out. A SVM classifier model is developed to map the complex nonlinear relationship between operating parameters and the quality indexes of fired pellet, and a genetic algorithm is adapted in the energy optimization with the fitness function based on the SVM classifier model. This method can reduce the energy consumption while maintaining the fired pellet quality stable. The results show that the accuracy of the SVM classifier model is satisfied and the gas consumption can be reduced by 4% per ton of green pellets with this optimization method.

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