Improving best route using intelligent Ad Hoc system

This study aims to studing possibility transferring data with short time, without or liitle cost and minimum lost of data This study attempts to find a system with high performance in sending and receiving message between nodes minimum lost with information using a genetic algorithm to improve this advantage. Main problem of our study with the system is how to decrease (cost and time) and improve it by intelligent function with GA create two or more back up of distributed node depend on time; route calculation saves as a backup map to direct switch without any delay when simulation execution indicated good result. Simulation results are carried out for both algorithms using MATLAB. The goal of our paper is process of data transferring with the most important three factors less expensive, less time and the least possible loss of transferred data.

[1]  Tommaso Caldognetto,et al.  Optimal control of Local Area Energy Networks (E-LAN) , 2018, Sustainable Energy, Grids and Networks.

[2]  Christiane Tammer.  Algorithms for the Solution of Optimization Problems , 2020 .

[3]  Martinus Dipobagio.  An Overview on Ad Hoc Networks , 2009 .

[4]  Yourim Yoon,et al.  Investigation of incremental hybrid genetic algorithm with subgraph isomorphism problem , 2019, Swarm Evol. Comput..

[5]  José R. Fernández,et al.  Hyperelastic characterization oriented to finite element applications using genetic algorithms , 2019, Adv. Eng. Softw..

[6]  Sanyang Liu,et al.  Face recognition based on genetic algorithm , 2019, J. Vis. Commun. Image Represent..

[7]  Sean Pascoe,et al.  An overview of genetic algorithms for the solution of optimisation problems , 1999 .

[8]  Edwin R. Hancock,et al.  Structural network inference from time-series data using a generative model and transfer entropy , 2019, Pattern Recognit. Lett..

[9]  Filomena Ferrucci,et al.  Speed up genetic algorithms in the cloud using software containers , 2019, Future Gener. Comput. Syst..

[10]  Martin T. Hagan,et al.  Neural network design , 1995 .

[11]  Jian-Guo Liu,et al.  Information interaction model for the mobile communication networks , 2019, Physica A: Statistical Mechanics and its Applications.

[12]  Benjamin Duraković,et al.  Continuous quality improvement in textile processing by statistical process control tools: A case study of medium-sized company , 2013 .

[13]  B. Duraković,et al.  Students’ entrepreneurial orientation intention, business environment and networking: insights from Bosnia and Herzegovina’ , 2016 .

[14]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[15]  Lance D. Chambers.  Practical handbook of genetic algorithms , 1995 .

[16]  Vladimir V. Mokshin,et al.  Adaptive genetic algorithms used to analyze behavior of complex system , 2019, Commun. Nonlinear Sci. Numer. Simul..

[17]  Konstantinos Poularakis,et al.  Flexible SDN control in tactical ad hoc networks , 2019, Ad Hoc Networks.

[18]  Benjamin Durakovic.  Emerging Issues, Trends and Challenges for Sustainable Engineering , 2017 .

[20]  B. Duraković.  Design of Experiments Application, Concepts, Examples: State of the Art , 2017 .

[21]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[22]  Md Zahidul Islam,et al.  Combining K-Means and a genetic algorithm through a novel arrangement of genetic operators for high quality clustering , 2018, Expert Syst. Appl..

[23]  Sudan Jha,et al.  Governing mobile Virtual Network Operators in developing countries , 2019, Utilities Policy.

[24]  Sukhpreet kaur Sukhpreet kaur.  An Overview of Mobile Ad hoc Network: Application, Challenges and Comparison of Routing Protocols , 2013 .

[25]  David Beasley,et al.  An overview of genetic algorithms: Part 1 , 1993 .