Dynamic Path Finding using Ant Colony Optimization
Reshma M1, Neena Thomas2, Surekha Mariam Varghese3

1Reshma M*, Computer Science and Engineering, Mar Athanasius College of Engineering, Kothamangalam, Ernakulam, India.
2Neena Thomas, Computer Science and Engineering, Mar Athanasius College of Engineering, Kothamangalam, Ernakulam, India.
3Dr. Surekha Mariam Varghese, Computer Science and Engineering, Mar Athanasius College of Engineering, Kothamangalam, Ernakulam, India.

Manuscript received on January, 2021. | Revised Manuscript received on January 15, 2021. | Manuscript published on January 30, 2021. | PP: 134-138 | Volume-9 Issue-5, January 2021. | Retrieval Number: 100.1/ijrte.E5210019521 | DOI: 10.35940/ijrte.E5210.019521
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Ant Colony Optimization (ACO) has been commonly applied in solving discrete optimization problems. This is an attempt to apply ACO in a dynamic environment for finding the optimal route. To create a dynamically changing scenario, in addition to distance, constraints such as air quality, congestion, user feedback, etc are also incorporated for deciding the optimal route. Max-Min Ant System (MMAS), an ACO algorithm is used to find the optimal path in this dynamic scenario. A local search parameter ε is also introduced in addition to ρ to improve the exploration of unused paths. Adaptability was studied by dynamically changing the costs associated with different parameters. 
Keywords: ACO, MMAS, Breezo Meter, Google Maps, Traffic Congestion.