Simulation-Annealing-Implement

This repository contains a rust implementation of the Simulated Annealing (SA) algorithm, a probabilistic technique for approximating the global optimum of a given function. Specifically, this implementation can be applied for solving the Traveling Salesman Problem (TSP).

Getting Started

Prerequisites

To compile and run this project, the following prerequisites are required:

• Rust: The Rust programming language.
• Cargo: The Rust package manager.
• Other Rust packages including: regex, rand, calamine and std.

Installation

Clone the repository to your local machine:

git clone https://github.com/DaLaw2/Simulation-Annealing-Implement.git

Navigate to the project directory:

cd Simulation-Annealing-Implement

Build the project:

cargo build

Usage

The algorithm can be executed from the command line with the following command:

cargo run -- --input=<path_to_input_file> --output=<path_to_output_file> --config=<path_to_config_file>

• --input: Specifies the path to the input Excel file (.xlsx) that contains the 2D coordinates of the points.
• --output: Specifies the path to the output text file where the results will be saved.
• --config: Specifies the path to the configuration text file that contains the parameters of the algorithm.

Configuration

The configuration file allows to tune the parameters of the SA algorithm:

• initial_temperature: The initial temperature of the system.
• minimum_temperature: The minimum temperature of the system.
• temperature_decay: The rate at which the temperature decays.
• max_iterations: The maximum number of iterations.
• generation_method: The method to generate the next solution. This can be Swap or Reverse.
• cooling_method: The method for cooling. This can be ExponentialMultiplicativeCooling, LogarithmicMultiplicativeCooling, LinearMultiplicativeCooling, or QuadraticMultiplicativeCooling.

Output

The program will produce an output text file with the following information:

• The current iteration count.
• The change in energy (ΔE).
• The current temperature (T).
• The probability of acceptance of a new solution.
• The final solution (a sequence of points representing a possible solution for the TSP).
• The total path length of the solution.
• The total execution time.