Open AccessExperimental Comparison between Genetic Algorithm and Ant Colony Optimization on Traveling Salesman Problem
Author(s) -
Muhammed Yaseen Morshed Adib,
Jannatun Razia,
Md. Toufiqur Rahman
Publication year - 2021
Publication title -
international journal of scientific research in science, engineering and technology
Language(s) - English
Resource type - Journals
eISSN - 2395-1990
pISSN - 2394-4099
DOI - 10.32628/ijsrset218135
Subject(s) - ant colony optimization algorithms , travelling salesman problem , meta optimization , swarm intelligence , mathematical optimization , genetic algorithm , metaheuristic , computer science , parallel metaheuristic , heuristic , algorithm , extremal optimization , mathematics , particle swarm optimization
This paper is based on bio-inspired optimization algorithms. Optimization is the process of selecting the best element by following some rules and criteria from some set of available alternatives. In this paper, we have solved Traveling Salesman Problem (TSP) using Swarm Intelligence algorithms and we have compared them. First we have implemented the basic Genetic Algorithm (GA) on TSP. Then we have implemented Ant Colony Optimization (ACO) Algorithm on TSP. In optimization problem, Genetic Algorithm (GA) and Ant Colony Optimization (ACO) Algorithm have been known as good meta-heuristic techniques. GA is designed by adopting the natural law of evolution, while ACO is inspired by the foraging behavior of ant species. Balancing the exploitation-exploration tradeoff is required in ACO. In contrast with the GA implementation, ACO was much easier to control.