The optimisation of travelling salesman problem based on parallel ant colony algorithm Online publication date: Tue, 07-Mar-2023
by Amin Jarrah; Ali S. Al Bataineh; Abedalmuhdi Almomany
International Journal of Computer Applications in Technology (IJCAT), Vol. 69, No. 4, 2022
Abstract: General search algorithms offer some solutions to solve and avoid the constraints of the finding shortest path problem. Ant Colony Optimisation (ACO) is a meta-heuristic search-based and probabilistic searching technique for an optimal path. However, ACO is computationally intensive and may not achieve the desired performance. Thus, a parallel implementation of the ACO for the travelling salesman problem is proposed and implemented on FPGA platform. Different optimisation techniques are adopted and applied to enhance the performance of the algorithm. The overall results have shown a 28-fold improvement in performance due to the applied optimisation techniques with different numbers of ants and iterations. The speedup is improved by increasing the number of iterations/ants which further validated the proposed method. The network on chip methodology was also adopted to connect the ants where a comprehensive analysis of FPGA chip traffic is modelled and emulated to get the best architecture.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computer Applications in Technology (IJCAT):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook