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R. Nallusamy ¹, K. Duraiswamy ²

Affiliations
1 Computer Science and Engineering Department, KS Rangasamy College of Technology, Tiruchengode-637215, IN
2 KS Rangasamy College of Technology, Tiruchengode-637215, IN

Abstract

This paper proposes a new Genetic algorithm with shrink wrap algorithm based initial population selection for the dynamic shortest path routing. Shortest path routing is the basic problem for number of real life problems. Different shortest path optimization problems can be solved by using various approximation algorithms due its computational complexity. The routing in packet switched multi-hop networks can be described as a classical combinatorial optimization problem i.e. a shortest path routing problem in graphs. The proposed algorithm shows that the GA and shrink wrap algorithms are best candidates for the optimization of dynamic shortest path routing problems due to their fastness in computation comparing to other soft computing and met heuristics algorithms.

Keywords

Combinatorial Optimization, Dynamic Shortest Path Routing Problem, Genetic Algorithm, Shrink Wrap Algorithm.

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R. Nallusamy ¹, K. Duraiswamy ²

Affiliations
1 Computer Science and Engineering Department, K.S. Rangasamy College of Technology, Tiruchengode-637215, IN
2 K.S. Rangasamy College of Technology, Tiruchengode-637215, IN

Abstract

This paper reviews the overview of the dynamic shortest path routing problem and the various neural networks to solve it. Different shortest path optimization problems can be solved by using various neural networks algorithms. The routing in packet switched multi-hop networks can be described as a classical combinatorial optimization problem i.e. a shortest path routing problem in graphs. The survey shows that the neural networks are the best candidates for the optimization of dynamic shortest path routing problems due to their fastness in computation comparing to other softcomputing and metaheuristics algorithms.

Keywords

Combinatorial Optimization, Dynamic Shortest Path Routing Problem, Neural Networks, Packet Switched Multi Hop Networks.

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