GENETIC ALGORITHM FOR OPTIMIZING SHORTEST PATH PROBLEMS IN NETWORK ROUTING Amarjit Singh Dhillon Ranjit Singh Saini Karan seth Rokibul Hassan
GENETIC ALGORITHM
FOR OPTIMIZING
SHORTEST PATH
PROBLEMS IN NETWORK
ROUTING
Amarjit Singh Dhillon Ranjit Singh Saini Karan seth Rokibul Hassan
DATA NETWORKS ROUTING
• Routing: Selecting minimum cost/distance paths for transferring packets from Source node to destination node
• Routing Is a complex process in large networks
• Two Types1. Static Routing2. Dynamic Routing
Source
Destination
Destination
PLANNING PROBLEM
• In static routing algorithms, the path used is fixed regardless of traffic conditions.
Problems :
1. No- Fault tolerance : If failure occurs, traffic will not be re-routed.
2. Time consuming for large network configuration: Manual configuration of each router takes a lot of time.
3. Prone to Human errors: As configuration is done manually.
SHORTEST PATH PLANNING PROCESS
• Finding a minimum cost path between a given number of nodes.
• Existing solutions include Dijkstra algorithm: used in OSPF
A. Inefficient for very large networks.B. A lot of computations need to be repeated for large networks.
O(|E| +|V|log|V|) – optimal method / best soluton
Moreover we are finding paths between two routers s.t all given router are to be covered – using meta-heuristic method
Dijkstra algorithm
INITIAL PARAMETERS - MODIFIED GENETIC ALGORITHM
Input Parameters1. Initial # of routers to be installed2. Initial population size3. # of generations
Outputs 4. 3-D graph showing initial positions of routers/ switches/ nodes.5. 3-D graph showing shortest path between initial and final path.6. 2-d graph showing shortest distance at all generations.
MGA ALGORITHM
a) Randomly generate 3-d co-ordinates for routers/ switches/ nodes.b) Randomly Generate population of initial routes.c) Generate distance – cost matrix; o For 1 : all generations {
a) Update distance – cost matrix among all nodes.b) Compute the fitness for each individual – i.e minimum value of cost;
c) Perform the selection Find Elite Individual on basis of min cost - best individual directly moved to next generation
d) DO Crossover e) if PC ( fixed ) > PRC (random ) { bifurcate initial pop into half and perform crossover }f) Else { skip Crossover }
g) Generate an selection vector to select child populationh) Select individuals in pair of 4 using selection vector and do mutation {
i) Do Mutation -j) if PM ( fixed ) > PRM (random ) { Swapping, Sliding, Flipping }k) Else { skip mutation } }
l) Initial population = new population } END
GENERATING POPULATION
DISTANCE COST MATRIX
RANDOM CROSSOVER / MUTATION PROBABILITY
CROSSOVER
USING SELECTION VECTOR
TWO POINT MUTATION - FLIPPING
SWAPPING & SLIDING
EXPERIMENT RESULTS
CONCLUSION
Provides solution in reasonable amount of time– using meta-heuristic method
Achieves global minima - for given # of generations
Mutation & crossover led to faster convergence
Multiple constraints can be implemented in MGA
REFERENCES
[1] R Kumar and M Kumar, “Exploring Genetic algorithm for shortest path optimization in data networks,” Global Journal of Computer Science, 2010.
[2] C. Ahn and R. S. Ramakrishna, “A genetic algorithm for shortest path routing problem and the sizing of populations,”Evolutionary Computation, IEEE Transactions on, vol. 6, no. 6, pp. 566–579, 2002.
[3] G. T. Nair and K. Sooda, “An intelligent routing approach using genetic algorithms for quality graded network,” International Journal of Intelligent Systems Technologies and Applications, vol. 11, no. 3–4, pp. 196–211, 2012.
[4] J Lee and J Yang, “A Fast and Scalable Re-routing Algorithm based on Shortest Path and Genetic Algorithms J. Lee, J. Yang Jungkyu Lee,” International Journal of Computers Communications & …, 2014.
CONCLUSION