Optimization of ACO-GA for Routing Optimization
Keywords:
Ant Colony Optimization, Genetic Algorithm, Routing Optimization, Meta-HeuristicAbstract
For network routing optimization many protocols and methods are evolved over time and this continuous race for more accurate and speedy data delivery is going on, our research is also one attempt on that race. Nature is best knowledge available on this planet, so ignoring this available science on any field would be foolish. Ants are social insects that searches food collectively and passed their knowledge of food path to others by leaving a hormone called pheromone on the way; researches used this earlier solely and with Genetics for routing optimization. Combination of both is used in routing optimization where output of GA is passed on ACO algorithm but we proposed to use firstly ACO and then passed the output of ACO in GA to get possible path. By our approach we not only reduced the domain of routes for GA but also optimized the time, every time GA uses for evolving best set of chromosomes. We just not only optimized the routing but also optimized earlier research of combination of ACO and GA.
References
GD. Caro, M. Dorigo, “Ant colonies for Adaptive Routing in Packet-Switched Communications Networks”, International Conference on Parallel Problem Solving from Nature, USA, pp 673-682 , 2006
M. Dorigo, “Optimization, Learning and Natural Algorithms”, PhD thesis from Politecnico di Milano, Italy, pp.1-13, 1992,
V.K. Ojha, A. Abraham, V. Snasel, “ACO for Continuous Function Optimization: A Performance Analysis”, 14th International Conference on Intelligent Systems Design and Applications (ISDA), Japan, pp.145-150, 2014.
GD. Caro, M. Dorigo, “AntNet : Distributed Stigmergetic Control For Communications Network”, Journal of Artificial Intelligence Research, Vol.9, Issue.1, pp.317-365, 1998.
X. Wang, J. Ma, J. Wang, “Parallel energy-efficient coverage optimization with maximum entropy clustering in wireless sensor networks”, Journal of Parallel and Distributed Computing, Vol.69, Issue.10, pp.838-847, 2007.
C.Saliba, RA. Farrugia, “Quality of Service Aware Ant Colony Optimization Routing Algorithm”, 15th IEEE Conference on Mediterranean Electrotechnical, Valletta, pp.343-347, 2010.
G.D. Caro, M. Dorigo, “AntNet: a mobile agents approach to adaptive routing”, Proceedings of the Thirty-First Hawaii International Conference on System Science, Belgium, pp.74-83, 1998.
M. Kaur, M. Agnihotri, "A Hybrid Technique Using Genetic Algorithm and ANT Colony Optimization for Improving in Cloud Datacenter", International Journal of Computer Sciences and Engineering, Vol.4, Issue.8, pp.100-105, 2016.
D. Mukherjee, S. Acharyya, “Ant Colony Optimization Technique Applied in Network Routing Problem”, International Journal of Computer Applications, Vol.1, Issue.15, pp.66-73, 2012.
M. Dorigo, “From Ant Colonies to Artificial Ants : First International Workshop on Ant Colony Optimization”, AI Communication. Vol.11, Issue.3, pp.39-42, 2000.
T. Stützle, “Parallelization Strategies for Ant Colony Optimization”, Proceedings of Fifth International Conference on Parallel Problem Solving from Nature, Springer-Verlag, Barlin, pp.722-731, 1998.
K. Deb, “An efficient constraint-handling method for genetic algorithms”, Comput. Methods Appl. Mech. Eng., vol.186, No.2, pp. 311-338, 2000.
Yuanyuan Zhang, “Multi-Objective Search - based Requirements Selection and ptimisation”, Ph.D Thesis of King’s College-University of London, London, pp.1- 276, 2010.
J. Gu, J. Hu, T. Zhao, G. Sun, “A new resource scheduling strategy based on genetic algorithm in cloud computing environment”, Journal of Computers, Vol.7, No.1, pp.42-52, 2012.
Abdul Kadar Muhammad Masum ,”Solving the Vehicle Routing Problem using Genetic Algorithm”, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 2, No. 7, pp.126-131, 2011.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
