Large-scale multi-objective optimisation for sustainable waste management using Evolutionary Algorithms
A meta-heuristic approach to sustainable waste management.
Make sure to append the ibmdecisionoptimization and conda-forge packages to the current channels. Write this into a terminal:
$ conda config --append channels ibmdecisionoptimization`
$ conda config --append channels conda-forge
Clone the repository and install the requirements file via conda.
$ git clone https://github.com/felixboelter/large-scale-waste-management-optimisation
$ cd large-scale-waste-management-optimisation
$ conda create --name <env> --file requirements.txt
The main.py file includes a class which generates and solves all of the graphs using the algorithms found in the source folder.
You can change the crossover and mutation probabilities by changing the crossover_probs and mutation_probs parameters with a list of selected probabilities between 0 and 1.
Solutions to every solved graph should be found in a created Results folder. Which will include:
- A csv file with the numeric solution of the best hypervolume solutions for each algorithm.
- The solved graph of the best hypervolume solution for each algorithm.
- A numpy file (.npy) containing all the solutions of the algorithms and decision values.