Experimental Comparison between Genetic Algorithm and Ant Colony Optimization on Traveling Salesman Problem-Reference-Cited by-同舟云学术

Experimental Comparison between Genetic Algorithm and Ant Colony Optimization on Traveling Salesman Problem

Published:2021-02-02 Issue: Volume: Page:155-162
ISSN:2394-4099
Container-title:International Journal of Scientific Research in Science, Engineering and Technology
language:en
Short-container-title:IJSRSET

Author:

Morshed Adib Muhammed Yaseen¹,Razia Jannatun²,Rahman Md. Toufiqur²

Affiliation:

1. Computer Science and Engineering, Stamford University Bangladesh, Dhaka-1217, Dhaka, Bangladesh

2. Computer Science and Engineering, Ahsanullah University o Science and Technology, Dhaka-1208, Dhaka, Bangladesh

Abstract

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.

Publisher

Technoscience Academy

Subject

General Medicine

Reference16 articles.

1. Dr. M. S. Alam, Continuous Optimization with evolutionary and swarm intelligence algorithms, PhD Thesis, Bangladesh University of Engineering and Technology, September 2013.

2. Bäck, T., Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford University Press,USA, 1996.

3. Mühlenbein, H., “The Breeder Genetic Algorithm – a provable optimal search algorithm and its application”, IEEE Colloquium on Applications of Genetic Algorithms, Digest No. 94/067, London, March 15, 1994.

4. Dorigo, M. and Stützle, T., Ant Colony Optimization. MIT Press, Cambridge, MA, 2004.

5. Li, K., Kang, L., Zhang, W., Li, B., (2008), Comparative Analysis of Genetic Algorithm and Ant Colony Algorithm on Solving Traveling Salesman Problem, , in IEEE International Workshop. Semantic Computing and Systems.