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Various algorithms for solving the Symmetric Traveling Salesman Problem, along with performance analysis

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Symmetric Traveling Salesman Problem Solver

This C project implements various algorithms for solving the Symmetric Traveling Salesman Problem (TSP), along with performance analysis tools. For more detailed information, please refer to the accompanying thesis file in repository. The thesis provides an in-depth explanation of the project and its analysis.

Objective

The primary objective of this project is to explore various algorithms and methodologies for efficiently solving the Traveling Salesman Problem (TSP).

Implemented Algorithms

Constructive Heuristics:

  • Greedy heuristic with GRASP
  • Extra-mileage heuristic
  • 2-OPT refining heuristic

Metaheuristics:

  • Tabu Search
  • VNS (Variable Neighborhood Search)
  • Simulated Annealing
  • Genetic Algorithm

Matheuristics:

  • Hard fixing
  • Local branching

Exact methods using CPLEX:

  • Benders' loop
  • Callback method

Performance Analysis

Performance profiles of the implemented methods are provided in the perf_prof folder. These profiles include computational performance metrics, solution quality, and convergence behavior.

Usage

  1. Installation: Clone the repository to your local machine.
    git clone https://github.com/elnurisg/Symmetric_TSP
  1. Compilation: Use the provided Makefile to compile the project.
    make
  1. Execution: Run the solver with the desired parameters.
    ./tsp <parameters>
  1. Performance Profiling: Explore performance profiles in the perf_prof folder for detailed analysis.

Dependencies

  • CPLEX Optimization Studio version 22.1.1
  • GNU Plot for visualization

Documentation

Implemented algorithms are documented with Doxygen for easy reference and understanding.

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Various algorithms for solving the Symmetric Traveling Salesman Problem, along with performance analysis

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