Open AccessPerformance Analysis of Genetic Algorithm, Particle Swarm Optimization and Ant Colony Optimization for solving the Travelling Salesman Problem
Author(s) -
Ravi Chandrika,
P. S. Goel,
Bhagyashree R. Bagwe
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.b1016.0782s419
Subject(s) - travelling salesman problem , ant colony optimization algorithms , metaheuristic , mathematical optimization , parallel metaheuristic , meta optimization , extremal optimization , traverse , particle swarm optimization , combinatorial optimization , multi swarm optimization , computer science , genetic algorithm , optimization problem , computation , 2 opt , swarm intelligence , ant colony , algorithm , mathematics , geodesy , geography
The Travelling salesman problem also popularly known as the TSP, which is the most classical combinatorial optimization problem. It is the most diligently read and an NP hard problem in the field of optimization. When the less number of cities is present, TSP is solved very easily but as the number of cities increases it gets more and more harder to figure out. This is due to a large amount of computation time is required. So in order to solve such large sized problems which contain millions of cities to traverse, various soft computing techniques can be used. In this paper, we discuss the use of different soft computing techniques like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and etc. to solve TSP.