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@@ -5,10 +5,12 @@ from heapq import heappush, heappop
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from random import shuffle, sample, randint, SystemRandom
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import matplotlib.pyplot as plt
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+# globals to contain reference solutions
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neos14 = [318, 324, 336, 319, 351, 311, 272, 361, 274, 322]
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neos15 = [313, 318, 281, 324, 378, 291, 348, 342, 353, 325]
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neos16 = [404, 353, 361, 349, 358, 344, 373, 355, 243, 330]
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neos36 = 464
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+neos = {14: neos14, 15: neos15, 16: neos16, 36: neos36}
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# returns a shuffled version of the cities list while maintaining the first & last elements
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def rand_order(cities):
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@@ -255,26 +257,33 @@ def main():
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plt.switch_backend('agg')
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solution_steps = []
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solution_scores = []
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+ good_sol_counts = []
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for i in range(14, 17):
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steps = []
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solution_steps.append(steps)
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scores = []
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solution_scores.append(scores)
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+ good_sols = []
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+ good_sol_counts.append(good_sols)
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for j in range(1, 11):
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filepath = "tsp_problems/" + str(i) + "/instance_" + str(j) + ".txt"
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prob_steps = 0
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prob_scores = 0
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+ good_solutions = 0
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# run problem 100 times
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for k in range(0, 100):
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# path, cost, steps returned
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result = solver(filepath, 1)
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prob_steps += result[2]
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- prob_scores += result[1]
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+ prob_scores += (result[1] / neos[i][j - 1])
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+ if (result[1] <= neos[i][j- 1]):
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+ good_solutions += 1
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steps.append(prob_steps / 100.0)
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scores.append(prob_scores / 100.0)
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+ good_sols.append(good_solutions)
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figure, axes = plt.subplots(3, 1, True)
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@@ -283,8 +292,8 @@ def main():
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axes[i].set_xlabel("Problem Instance w/ " + str(i + 14) + " cities")
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axes[i].set_ylabel("Steps Taken", color = 'b')
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axis_twin = axes[i].twinx()
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- axis_twin.plot(range(1, 11), solution_scores[i], label = "Solution Cost/Distance", color = 'r')
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- axis_twin.set_ylabel("Distance in units", color = 'r')
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+ axis_twin.plot(range(1, 11), solution_scores[i], label = "Solution Quality", color = 'r')
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+ axis_twin.set_ylabel("Solution Quality (dist / reference)", color = 'r')
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#axes[i].legend()
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#axis_twin.legend()
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tarfeef101