This project attempts to solve 3D path planning for amazon drones using Genetic Algorithms and Ant Colony Optimization

Contains the logic for Ant Colony Optimization

Contains the logic for Genetic Algorithms Optimization

Contains the logic for generating graphs used to test the algorithms

python acosolver.py --graph <map file> --goals <node id 1> <node id 2> ...

graph: the graph file used for the algorithm. E.g: waterloo.ecegraph

goals: the finishing node for each drone. All the drones start at node 1

cd ga

python main.py --graph <map file> --goals <node id 1> <node id 2> ...

graph: the graph file used for the algorithm. E.g: ../waterloo.ecegraph

goals: the finishing node for each drone. All the drones start at node 1

python aco_ga_waterloo.py

this will run both the ACO and GA algorithm using the waterloo.ecegraph map with goals set to 226, 228, and 230

The parameters for the GA can be changed in ga/gaconstants.py

The parameters for the ACO can be changed in aco/acoconstants.py

NOTE: The list of figures, list of tables, and references do not count towards out report length of 6 pages.