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 Strategic Team AI Path Plans: Probabilistic Pathfinding

2008 ◽  
Vol 2008 ◽  
pp. 1-6 ◽  
Author(s):  
Tng C. H. John ◽  
Edmond C. Prakash ◽  
Narendra S. Chaudhari
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Computer Games ◽  
Fitness Function ◽  
New Method ◽  
High Quality ◽  
Plan Generation ◽  
Novel Method ◽  
Games And Simulations

This paper proposes a novel method to generate strategic team AI pathfinding plans for computer games and simulations using probabilistic pathfinding. This method is inspired by genetic algorithms (Russell and Norvig, 2002), in that, a fitness function is used to test the quality of the path plans. The method generates high-quality path plans by eliminating the low-quality ones. The path plans are generated by probabilistic pathfinding, and the elimination is done by a fitness test of the path plans. This path plan generation method has the ability to generate variation or different high-quality paths, which is desired for games to increase replay values. This work is an extension of our earlier work on team AI: probabilistic pathfinding (John et al., 2006). We explore ways to combine probabilistic pathfinding and genetic algorithm to create a new method to generate strategic team AI pathfinding plans.

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.

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.

An Improved Genetic Algorithm Based Annulus-sector Clustering Routing Protocol for Wireless Sensor Networks

2021 ◽  
Author(s):  
Wang Chu-hang ◽  
Liu Xiao-li ◽  
Youjia Han ◽  
Hu Huang-shui ◽  
Wu Sha-sha
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Routing Protocol ◽  
Fitness Function ◽  
Minimum Energy ◽  
Wireless Sensor ◽  
Optimal Routing ◽  
Improved Genetic Algorithm

Abstract In wireless sensor networks, uniform cluster formation and optimal routing paths finding are always the two most important factors for clustering routing protocols to minimize the network energy consumption and balance the network load. In this paper, an improved genetic algorithm based annulus-sector clustering routing protocol called GACRP is proposed. In GACRP, the circular network is divided into sectors with the same size for each annulus, whose number is determined by calculating the minimum energy consumption of each annulus. Each annulus-sector forms a cluster and the best node in this annulus-sector is selected as cluster head. Moreover, an improved genetic algorithm with a novel fitness function considering energy and load balance is presented to find the optimal routing path for each CH, and an adaptive round time is calculated for maintaining the clusters. Simulation results show that GACRP can significantly improve the network energy efficiency and prolong the network lifetime as well as mitigate the hot spot problem.

scholarly journals Optimization of High-Speed Train Control Strategy for Traction Energy Saving Using an Improved Genetic Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Ruidan Su ◽  
Qianrong Gu ◽  
Tao Wen
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Control Strategy ◽  
High Speed ◽  
Energy Savings ◽  
Fitness Function ◽  
High Speed Train ◽  
Improved Genetic Algorithm ◽  
Running Time ◽  
Train Control

A parallel multipopulation genetic algorithm (PMPGA) is proposed to optimize the train control strategy, which reduces the energy consumption at a specified running time. The paper considered not only energy consumption, but also running time, security, and riding comfort. Also an actual railway line (Beijing-Shanghai High-Speed Railway) parameter including the slop, tunnel, and curve was applied for simulation. Train traction property and braking property was explored detailed to ensure the accuracy of running. The PMPGA was also compared with the standard genetic algorithm (SGA); the influence of the fitness function representation on the search results was also explored. By running a series of simulations, energy savings were found, both qualitatively and quantitatively, which were affected by applying cursing and coasting running status. The paper compared the PMPGA with the multiobjective fuzzy optimization algorithm and differential evolution based algorithm and showed that PMPGA has achieved better result. The method can be widely applied to related high-speed train.

scholarly journals Energy Efficient Routing Protocol in Sensor Networks Using Genetic Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7060
Author(s):  
Jatinkumar Patel ◽  
Hosam El-Ocla
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Routing Protocol ◽  
Routing Protocols ◽  
Ad Hoc ◽  
Fitness Function ◽  
Delivery Ratio ◽  
Ad Hoc Routing ◽  
Energy Efficient Routing

In this paper, we examine routing protocols with the shortest path in sensor networks. In doing this, we propose a genetic algorithm (GA)-based Ad Hoc On-Demand Multipath Distance Vector routing protocol (GA-AOMDV). We utilize a fitness function that optimizes routes based on the energy consumption in their nodes. We compare this algorithm with other existing ad hoc routing protocols including LEACH-GA, GA-AODV, AODV, DSR, EPAR, EBAR_BFS. Results prove that our protocol enhances the network performance in terms of packet delivery ratio, throughput, round trip time and energy consumption. GA-AOMDV protocol achieves average gain that is 7 to 22% over other protocols. Therefore, our protocol extends the network lifetime for data communications.

scholarly journals Reverse Flood Routing in an Open Channel Using Genetic Algorithm

2021 ◽  
Author(s):  
Ali Azizipour ◽  
Seyed Mahmood Kashefipour ◽  
Ali Haghighi
Keyword(s):  
Genetic Algorithm ◽  
Open Channel ◽  
Fitness Function ◽  
Flood Routing ◽  
Integrated Method ◽  
Karun River ◽  
The Status ◽  
Genetic Algorithm Method ◽  
Routing Method ◽  
Hydrometric Station

Flood routing in flood forecasting issue, calculation the height of flood bands, determining the river boundaries, and estimation of protective facilities for flood –exposed building is applicable. In many cases, due to the lack of measuring stations, the status of the upstream flood generating hydrograph is not known. The purpose of this study is to present an integrated method comprising of an optimization model and a hydrodynamic numerical model for flood modeling to determine the upstream hydrograph using the provided hydrograph at the downstream measuring station of a river. The routing procedure consists of three steps: (1) generating a hypothetical upstream hydrograph using genetic algorithm method; (2) hydrodynamic modeling using a numerical simulation model for flood routing according to the hypothetical hydrograph which is generated in the first step; (3) compare the calculated and observed hydrograph in downstream by using a fitness function. This recommended procedure was named as Reverse Flood Routing Method (RFRM) and was then applied to Karun River, the largest river in Iran. Comparing the generated upstream hydrograph by the RFRM model with the corresponding measured hydrograph at Ahvaz hydrometric station, as an ungauged river location, shows the high accuracy of the recommended model in this study.

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. 

MOEAGAC: an energy aware model with genetic algorithm for efficient scheduling in cloud computing

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nageswara Prasadhu Marri ◽  
N.R. Rajalakshmi
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machines ◽  
Fitness Function ◽  
Research Work ◽  
Energy Aware ◽  
Content Type ◽  
Multi Objective ◽  
Proposed Model

PurposeMajority of the research work either concentrated on the optimization of scheduling length and execution cost or energy optimization mechanism. This research aims to propose the optimization of makespan, energy consumption and data transfer time (DTT) by considering the priority tasks. The research work is concentrated on the multi-objective approach based on the genetic algorithm (GA) and energy aware model to increase the efficiency of the task scheduling.Design/methodology/approachCloud computing is the recent advancement of the distributed and cluster computing. Cloud computing offers different services to the clients based on their requirements, and it works on the environment of virtualization. Cloud environment contains the number of data centers which are distributed geographically. Major challenges faced by the cloud environment are energy consumption of the data centers. Proper scheduling mechanism is needed to allocate the tasks to the virtual machines which help in reducing the makespan. This paper concentrated on the minimizing the consumption of energy as well as makespan value by introducing the hybrid algorithm called as multi-objective energy aware genetic algorithm. This algorithm employs the scheduling mechanism by considering the energy consumption of the CPU in the virtual machines. The energy model is developed for picking the task based on the fitness function. The simulation results show the performance of the multi-objective model with respect to makespan, DTT and energy consumption.FindingsThe energy aware model computes the energy based on the voltage and frequency distribution to the CPUs in the virtual machine. The directed acyclic graph is used to represent the task dependencies. The proposed model recorded 5% less makespan compared against the MODPSO and 0.7% less compared against the HEFT algorithms. The proposed model recorded 125 joules energy consumption for 50 VMs when all are in active state.Originality/valueThis paper proposed the multi-objective model based on bio-inspired approach called as genetic algorithm. The GA is combined with the energy aware model for optimizing the consumption of the energy in cloud computing. The GA used priority model for selecting the initial population and used the roulette wheel selection method for parent selection. The energy model is used as fitness function to the GA for selecting the tasks to perform the scheduling.

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