Simulated Annealing Algorithms Python Implementation

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)

Installation

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

Usage

Configurations

$N$ - Number of iterations to perform

$func$ - Target function to apply the simulated annealing algorithms

$algo$ - Algorithm applied to optimize on the target function

$initial _ x$ - Initial value assigned to start the iterations.

The program is designed to run $N$ iterations of the applied algorithms on the target function to calculate its average best value and standard deviation as a direct conparison for different variations of the simulated annealing algorithm.

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 $N$ runs of the algorithm.

Results

The results can be found in the report, with more details on the analysis of the different algorithms.

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

License

MIT