def testMultipleStartLocalSearch(testerRuns, algorithmRuns):
data = utils.readTspFile("../data/kroA200.tsp")
paths = []
times = []
for _ in range(testerRuns):
start = time.time()
paths.append(multipleStartLocalSearch(data, algorithmRuns))
end = time.time()
times.append(end - start)
minRun = min(paths, key=lambda t: t[0])
maxRun = max(paths, key=lambda t: t[0])
distances = [p[0] for p in paths]
averageDistance = sum(distances) / len(distances)
minTime = min(times)
maxTime = max(times)
averageTime = sum(times) / len(times)
print("minimalny dystans: " + str(minRun[0]),
"średni dystans: " + str(averageDistance),
"maksymalny dystans: " + str(maxRun[0]))
print("minimalny czas: " + str(minTime),
"maksymalny czas: " + str(maxTime),
"średni czas: " + str(averageTime))
utils.drawGraph(data, minRun[1])
def main():
# instance = load(f'../data/kroA100.tsp')
# distances = np.array(utils.makeDistanceMatrix(instance))
# print(distances)
# print(random(distances, "edges"))
instance = load(f'../data/kroA100.tsp')
kroA100Data = utils.readTspFile("../data/kroA100.tsp")
utils.localSearchTester(random, kroA100Data, "vertices")
def testIteratedLocalSearch(testerRuns, algorithmStopTime):
data = utils.readTspFile("../data/kroB200.tsp")
paths = []
for _ in range(testerRuns):
paths.append(iteratedLocalSearch(data, algorithmStopTime))
minRun = min(paths, key=lambda t: t[0])
maxRun = max(paths, key=lambda t: t[0])
distances = [p[0] for p in paths]
averageDistance = sum(distances) / len(distances)
print("minimalny dystans: " + str(minRun[0]),
"średni dystans: " + str(averageDistance),
"maksymalny dystans: " + str(maxRun[0]))
utils.drawGraph(data, minRun[1])
def test_evolutionary(testerRuns, algorithmStopTime):
data = utils.readTspFile("../data/kroA200.tsp")
distanceMatrix = utils.makeDistanceMatrix(data)
paths = []
for _ in range(testerRuns):
paths.append(
evolutionaryTravelingSalesman(distanceMatrix, algorithmStopTime))
minRun = min(paths, key=lambda t: t[0])
maxRun = max(paths, key=lambda t: t[0])
distances = [p[0] for p in paths]
averageDistance = sum(distances) / len(distances)
print("minimalny dystans: " + str(minRun[0]),
"średni dystans: " + str(averageDistance),
"maksymalny dystans: " + str(maxRun[0]))
utils.drawGraph(data, minRun[1])
def tests(function):
data = readTspFile("../data/kroB200.tsp")
mode = "edges"
distanceMatrix = makeDistanceMatrix(data)
distances = np.array(makeDistanceMatrix(data))
paths = []
for _ in range(1000):
paths.append(function(distances, mode))
distances = []
for path in paths:
distances.append(calculatePathDistance(path, distanceMatrix))
minDistance = min(distances)
minPath = paths[distances.index(minDistance)]
print("minimalny dystans: " + str(minDistance))
similarityToBestVertices = np.zeros(1000)
similarityToEveryVerticesMatrix = np.zeros((1000, 1000))
similarityToBestEdges = np.zeros(1000)
similarityToEveryEdgesMatrix = np.zeros((1000, 1000))
for i, path in enumerate(paths):
for i_i, vertex in enumerate(minPath):
if vertex in path:
similarityToBestVertices[i] += 1
if minPath[i_i - 1] in path and minPath[i_i] in path:
index1 = np.where(path == minPath[i_i - 1])[0][0]
index2 = np.where(path == minPath[i_i])[0][0]
if path[index2 - 1] == path[index1]:
similarityToBestEdges[i] += 1
similarityToBestVertices[i] = similarityToBestVertices[i] / len(path)
similarityToBestEdges[i] = similarityToBestEdges[i] / len(path)
for j, otherPath in enumerate(paths):
for j_j, v in enumerate(path):
if v in otherPath:
similarityToEveryVerticesMatrix[i, j] += 1
if path[j_j - 1] in otherPath and path[j_j] in otherPath:
index1 = np.where(otherPath == path[j_j - 1])[0][0]
index2 = np.where(otherPath == path[j_j])[0][0]
if path[index2 - 1] == path[index1]:
similarityToEveryEdgesMatrix[i, j] += 1
similarityToEveryVerticesMatrix[i] = [
s / len(path) for s in similarityToEveryVerticesMatrix[i]
]
similarityToEveryEdgesMatrix[i] = [
s / len(path) for s in similarityToEveryEdgesMatrix[i]
]
similarityToEveryVertices = [
np.sum(s) / len(paths[0]) for s in similarityToEveryVerticesMatrix
]
similarityToEveryEdges = [
np.sum(s) / len(paths[0]) for s in similarityToEveryEdgesMatrix
]
plt.scatter(distances, similarityToBestVertices)
plt.xlabel("distances")
plt.ylabel("similarity")
plt.show()
plt.clf()
plt.scatter(distances, similarityToBestEdges)
plt.xlabel("distances")
plt.ylabel("similarity")
plt.show()
plt.clf()
plt.scatter(distances, similarityToEveryVertices)
plt.xlabel("distances")
plt.ylabel("similarity")
plt.show()
plt.clf()
plt.scatter(distances, similarityToEveryEdges)
plt.xlabel("distances")
plt.ylabel("similarity")
plt.show()
plt.clf()
def GreedyCycle(distanceMatrix, startingVertex):
path = [startingVertex]
for i in range(math.ceil(len(distanceMatrix) / 2)):
minCost = np.uint64(-1)
minCostIndex = -1
vertexToAdd = -1
for j, item in enumerate(range(len(distanceMatrix))):
if item not in path:
for n in range(len(path)):
distance1 = distanceMatrix[item][path[n - 1]]
distance2 = distanceMatrix[item][path[n]]
cost = distance1 + distance2 - distanceMatrix[path[n - 1]][
path[n]]
if cost < minCost:
minCostIndex = n
vertexToAdd = item
minCost = cost
path.insert(minCostIndex, vertexToAdd)
path = path + [path[0]]
return path
if __name__ == '__main__':
kroA100Data = utils.readTspFile("../data/kroA100.tsp")
kroB100Data = utils.readTspFile("../data/kroB100.tsp")
utils.tester(GreedyCycle, kroA100Data)
def main():
kroA100Data = utils.readTspFile("../data/kroA100.tsp")
utils.localSearchTester(steepest, kroA100Data, "edges")
