用于 TSP 问题的GRASP（贪婪随机 自适应 搜索过程）函数_python_代码_下载

# Metaheuristic-Local_Search-GRASP GRASP (Greedy Randomized Adaptive Search Procedure) 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. * rcl = List of candidate solutions. The Default Value is 5. * greediness_value = Chance of improving a candidate solution or to generate a random one. The Default Value is 0.5. * 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). # Other Metaheuristics Try online in the **Colab** my new library - [pyCombinatorial](https://github.com/Valdecy/pyCombinatorial). - 2-opt ([ Colab Demo ](https://colab.research.google.com/drive/1SLkM8r_VdlFCpNpm-2yTfr_ynSC5WIX9?usp=sharing)) - 2-opt Stochastic([ Colab Demo ](https://colab.research.google.com/drive/1xTm__7OwQVC_KX2b-eExLGgG1DgnJ10a?usp=sharing)) - 3-opt ([ Colab Demo ](https://colab.research.google.com/drive/1iAZLawLBZ-7yaPCyobMtel1SvBamxtjL?usp=sharing)) - 4-opt ([ Colab Demo ](https://colab.research.google.com/drive/1N8HKhVY4s20sfqo8IWIaCY-NHVk6gARS?usp=sharing)) - Ant Colony Optimization ([ Colab Demo ](https://colab.research.google.com/drive/1O2qogrjE4mZUZX3nsSxw43crumlBnd-D?usp=sharing)) - Extremal Optimization ([ Colab Demo ](https://colab.research.google.com/drive/1Y5YH0eYKjr1nj_IfhJXaILRDIXm-LWLs?usp=sharing)) - GRASP ([ Colab Demo ](https://colab.research.google.com/drive/1OnRyCc6C_QL6wr6-l5RlQI4eGbMdwuhS?usp=sharing)) - Guided Search ([ Colab Demo ](https://colab.research.google.com/drive/1uT9mlDoo37Ni7hqziGNELEGQCGBKQ83o?usp=sharing)) - Iterated Search ([ Colab Demo ](https://colab.research.google.com/drive/1U3sPpknulwsCUQq9mK7Ywfb8ap2GIXZv?usp=sharing)) - Scatter Search ([ Colab Demo ](https://colab.research.google.com/drive/115Ql6KegvOjlNUUfsbY4fA8Vab-db26N?usp=sharing)) - Stochastic Hill Climbing ([ Colab Demo ](https://colab.research.google.com/drive/1_wP6vg4JoRHGItGxEtXcf9Y9OuuoDlDl?usp=sharing)) - Tabu Search ([ Colab Demo ](https://colab.research.google.com/drive/1SRwQrBaxkKk18SDvQPy--0yNRWdl6Y1G?usp=sharing)) - Variable Neighborhood Search ([ Colab Demo ](https://colab.research.google.com/drive/1yMWjYuurzpcijsCFDTA76fAwJmSaDkZq?usp=sharing))