52 lines
1.9 KiB
Python
52 lines
1.9 KiB
Python
import glob
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import pandas as pd
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from aco.io_tsp import ProblemInstance
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from aco.TSP_solver import TSPSolver
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import os
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import sys
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def run(show_plots=False, verbose=False):
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if sys.argv[1] == "all":
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os.system("rm -f " + " ".join(glob.glob("sol/*.sol") + glob.glob("c_prob/*.txt")))
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os.system("c++ -O2 -lpthread --std=c++11 -o c_prob/aco aco.cc opt.cc")
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else:
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os.system("rm -f " + " ".join(glob.glob("c_prob/*.txt")))
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problems = glob.glob('./problems/*.tsp') if sys.argv[1] == "all" else ["./problems/"+sys.argv[1]+".tsp"]
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results = []
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index = []
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for problem_path in problems:
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prob_instance = ProblemInstance(problem_path)
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if verbose:
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prob_instance.print_info()
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if show_plots:
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prob_instance.plot_data()
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solver = TSPSolver(prob_instance)
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solution = solver.compute_solution(verbose=verbose)
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if verbose:
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print(f"the total length for the solution found is {solver.found_length}",
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f"while the optimal length is {solver.problem_instance.best_sol}",
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f"the gap is {solver.gap}%",
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f"the solution is found in {solver.duration} seconds", sep="\n")
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index.append((problem_path, "'C++ ant colony optimization'"))
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results.append([solver.found_length,
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solver.problem_instance.best_sol,
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solver.gap,
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solver.duration])
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with open("sol/" + prob_instance.name + ".sol", "w") as f:
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print(list(map(lambda x: x + 1, solution)), file=f)
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if show_plots:
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solver.plot_solution()
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return pd.DataFrame(results, index=index,
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columns=["tour length", "optimal solution",
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"gap", "time to solve"])
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if __name__ == '__main__':
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df = run(show_plots=False, verbose=True)
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df.to_csv("./results.csv")
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