/Metaheuristic-Local_Search-Variable_Neighborhood_Search

Variable Neighborhood Search Function for TSP problems

Primary LanguagePython

Metaheuristic-Local_Search-Variable_Neighborhood_Search

Variable Neighborhood Search Function for TSP problems. The function returns: 1) A list with the order of the cities to visit, and the total distance for visiting this same list order.

  • X = Distance Matrix.

  • buid_distance_matrix (HELPER FUNCTION) = Tranforms coordinates in a distance matrix (euclidean distance).

  • city_tour = Initial list of visitation.

  • seed (HELPER FUNCTION) = Generates a random list of visitation.

  • iterations = Total number of iterations. The Default Value is 5.

  • neighbourhood_size = Population size. The Default Value is 5.

  • max_attempts = Max number of attempts to improve solution before changing the neighborhood. The Default Value is 20.

  • plot_tour_distance_matrix (HELPER FUNCTION) = A projection is generated based on the distance matrix. The estimated projection may present a plot with path crosses, even for the 2-opt optimal solution (Red Point = Initial city; Orange Point = Second City).

  • plot_tour_coordinates (HELPER FUNCTION) = Plots the 2-opt optimal solution (Red Point = Initial city; Orange Point = Second City).

Single Objective Optimization

For Single Objective Optimization try pyMetaheuristic

Multiobjective Optimization or Many Objectives Optimization

For Multiobjective Optimization or Many Objectives Optimization try pyMultiobjective

TSP (Travelling Salesman Problem)

For Travelling Salesman Problems try pyCombinatorial