ML-MMAS: self-learning ant colony optimization for multi-criteria journey planning
Ant Colony Optimization (ACO) algorithms have been widely employed for solving optimization problems. Their ability to find optimal solutions depends heavily on the parameterization of the pheromone trails. However, the pheromone parameterization mechanisms in existing ACO algorithms have two major...
| Main Authors: | , , , |
|---|---|
| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2022
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/163885 |