print("\nPlease input name of file containing city locations ")
filename = input("Format: <filename.csv> ")
df_TSP = pd.read_csv(filename)
print("File with name " + filename + " found!")
except FileNotFoundError:
print("The file with name " + filename + " does not exist!")
sys.exit()
return df_TSP, filename
df_TSP, filename = read_file(
) # to see comparison of optimisation methods I have used I2.csv
Solution1 = Solution(df_TSP, filename)
Solution1.InitialSolution()
cost = Solution1.Cost()
cost1, sol1, test1 = Solution1.HC() # Method 1
Solution2 = Solution(df_TSP, filename)
Solution2.InitialSolution()
cost2, sol2, test2 = Solution2.HC_Swap_Insert_Reverse() # Method 2
# simulated annealing method yields the lowest cost
# but takes a lot of computation time
Solution3 = Solution(df_TSP, filename)
Solution3.InitialSolution()
cost3, sol3, test3 = Solution3.SimulatedAnnealing() # Method 3
# CONCLUSION:
# Method 2 (Hill Climbing method with swap/insert/revert at random ) gives worse solution because
# it cannot produce a better solution than my initial solution obtained using GREEDY algorithm
# Method 1 (Hill Climbing method) obtains 2nd best solution but it is more efficient optimising solution
