Self python implementation of simulated annealing algorithms analyzed in SIMULATED ANNEALING: A REVIEW AND A NEW SCHEME by Thomas Guilmeau, Emilie Chouzenoux, and Víctor Elvira. Including the following algorithms:
- Simulated Annealing (SA)
- Fast Simulated Annealing (FSA)
- Sequential Monte Carlo Simulated Annealing (SMC-SA)
- Curious Simulated Annealing (CSA)
To download the source code of the project, you need to clone this git repo.
git clone https://github.com/happytree718/ECE490_Project.git
Then, if you don't have the NumPy library installed on your machine, you need to install it as well.
pip install numpy
Configurations
The program is designed to run
Execution
To run the script, run the following command in the terminal.
python result.py
The program will output two numbers, which are the average best value and its corresponding standard deviation of
The results can be found in the report, with more details on the analysis of the different algorithms.
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.