Practical applications of simulated annealing - global optimization

Make sure to have packages listed in requirements.txt.
You can install them by running pip3 install -r requirements.txt (or pip - depends on your system).

Iris dataset classification

Optimization of the neural network weights with simulated annealing to solve classification task on the iris dataset.

All of the code is done in iris_classification.ipynb file.

To run the code please consider using jupyter notebook or google colab to execute the notebook content.

Travelling salesman problem solution

Solution for the traveling salesman problem using simulated annealing global optimization algorithm (combinatorial optimization).

Dataset of russian cities was taken from here. Top 30 most populated cities were considered. Dataset path is data/city.csv.

All of the code is done in travelling_salesman.ipynb file.

To run the code please consider using jupyter notebook or google colab to execute the notebook content.

Here is the visualization of the algorithm's work (images/algo_animation.gif).