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