QuadraticAssignmentProblem/main.py at master · kislN/QuadraticAssignmentProblem · GitHub

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from algorithms.local_search import LocalSearch
from algorithms.iterated import Iterated
from algorithms.guided import Guided
from tools.load_data import get_case
import numpy as np
import timeit
from tqdm import tqdm


paths = ['./data/benchmarks/tai20a', './data/benchmarks/tai40a', './data/benchmarks/tai60a',
         './data/benchmarks/tai80a', './data/benchmarks/tai100a']

for path in paths[0:1]:
    for i in tqdm(range(163, 164)):
        np.random.seed(i)
        data = get_case(path)
        algo = LocalSearch(*data.values())
        method = 'best_improvement'
        print(method)
        print(path.rsplit('/', maxsplit=1)[-1], ' result:')
        start = timeit.default_timer()
        result = algo.run(method)
        end = timeit.default_timer()
        print(result)
        print(f"Cost: {algo.cost_fun}")
        print('Time spent: {:.4f} seconds\n'.format(end-start))
        algo.plot()

    # method = 'best_improvement'
    # print(method)
    # print(path.rsplit('/', maxsplit=1)[-1], ' result:')
    # start = timeit.default_timer()
    # result = algo.run(method)
    # end = timeit.default_timer()
    # print(result)
    # print(f"Cost: {algo.cost_fun}")
    # print('Time spent: {:.4f} seconds\n'.format(end-start))

    # for i in tqdm(range(1000)):
    #     np.random.seed(i)
    #     algo = Guided(*data.values())
    #     method = 'best_improvement'
    #     start = timeit.default_timer()
    #     result = algo.run(method, iters=100, mu=1)
    #     end = timeit.default_timer()
    #     if algo.cost_fun < 710000:
    #         print('Guided ', method)
    #         print('i: ', i)
    #         print(path.rsplit('/', maxsplit=1)[-1], ' result:')
    #         print(result)
    #         print(f"Cost: {algo.cost_fun}")
    #         print('Time spent: {:.4f} seconds\n'.format(end - start))