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Evolutionary Intelligence

, Volume 12, Issue 2, pp 179–188 | Cite as

Hybrid optimizer for the travelling salesman problem

  • Sudip Kumar SahanaEmail author
Research Paper
  • 52 Downloads

Abstract

In this paper, a hybrid model which combines genetic algorithm and heuristics like remove-sharp and local-opt with ant colony system (ACS) has been implemented to speed-up convergence as well as positive feedback and optimizes the search space to generate an efficient solution for complex problems. This model is validated with well-known travelling salesman problem (TSP). Finally, performance and complexity analysis show that proposed nested hybrid ACS has faster convergence rate than other standard existing algorithms such as exact and approximation algorithms to reach the optimal solution. The standard TSP problems from the TSP library are also tested and found satisfactory.

Keywords

Ant colony optimization (ACO) Travelling salesman problem (TSP) Genetic algorithm (GA) Heuristics Hybrid approach Pheromone 

Notes

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Birla Institute of Technology, MesraRanchiIndia

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