stjordanis / trip-optimizer

Travel Time Optimization via Ant Colony and Genetic Evolution

Travel Time Optimization via Ant Colony and Genetic Evolution

Requirements

• python3
• matplotlib
• numpy
• pandas
• xgboost
• geopy

Installation

$ python3 -m pip install xgboost
$ python3 -m pip install geopy

Usage

Data Visualization

$ python3 basicAnalysis.py
$ python3 sophisticatedAnalysis.py

Predictive Modeling

$ python3 XGBoost.py

Evolutionary Algorithms

$ python3 aco_main.py -h
usage: aco_main.py [-h] [--verbose] loc_count ant_count g alpha beta rho q

positional arguments:
  loc_count   number of locations (default is 15)
  ant_count   number of ants to use (default is 10)
  g           number of generations (default is 100)
  alpha       relative importance of pheromone (default is 1.0)
  beta        relative importance of heuristic information (default is 10.0)
  rho         pheromone residual coefficient (default is 0.5)
  q           pheromone intensity (default is 10.0)

optional arguments:
  -h, --help  show this help message and exit
  --verbose   print out each generation cost and best path

$ python3 genetic_evo_main.py -h
usage: genetic_evo_main.py [-h] [--verbose] loc_count n g m c

positional arguments:
  loc_count   number of locations (default is 15)
  n           population size (default is 10)
  g           number of generations (default is 100)
  m           mutation factor (default is 0.5)
  c           crossover rate (default is 0.7)

optional arguments:
  -h, --help  show this help message and exit
  --verbose   print out each generation cost and best path

Output