| Title |
Aco With Reinforcement Learning Applied To Rescues Operations On Urban Forests |
| ID_Doc |
6075 |
| Authors |
Alves C.; Mendonça I.; De Almeida Guimarães V.; González P.H. |
| Year |
2024 |
| Published |
2024 IEEE Congress on Evolutionary Computation, CEC 2024 - Proceedings |
| DOI |
http://dx.doi.org/10.1109/CEC60901.2024.10612050 |
| Abstract |
In the context of smart cities where green infras-tructure is incentived, besides important benefits like regulating temperatures and absorbing pollutants among others, tour by urban forests is a way to experience closer contact with nature near of big urban centers. Eventually, visitors get lost, and helping these people with velocity is important to avoid severe incidents. Normally, rescue operations mobilize firefighters, ex-pensive equipment like helicopters and public resources. Following that idea of reducing search time in rescue operations, this paper considers the Data Mule Routing Problem with Limited Autonomy (DMRP-wLA). To find high-quality solutions, this paper proposes an Ant Colony Optimization algorithm enhanced with Reinforcement Learning to create an adaptive decision-making algorithm. © 2024 IEEE. |
| Author Keywords |
Ant Colony Optimization; Reinforcement Learning; Rescue Operations |
